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REVIEW 3 major objections 4 minor 259 references

ClassComet: Exploring and Designing AI-generated Danmaku in Educational Videos to Enhance Online Learning

T0 review · 3 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Large multimodal models can generate danmaku comments for educational videos comparable to human-created ones, and videos combining content- and emotion-related danmaku significantly raise engagement and learning gains.

desk verdict Fresh systems contribution to AI-generated danmaku for learning, but the headline learning-outcome claim is built on misreported statistics and a possible video-condition confound. read the letter →

arxiv 2504.18189 v1 pith:R6DFG2DB submitted 2025-04-25 cs.HC

Zipeng Ji , Pengcheng An , Jian Zhao This is my paper · ORCID
classification cs.HC
keywords danmakuvideo-basedlearninglargemultimodalmodelsAI-generatedcommentslearnerengagementoutcomesvirtualpersonasonlineeducation
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

Danmaku—live, timestamped viewer comments that scroll over a video—can make online lectures feel social, but most educational videos have too few viewers to produce any. The paper argues that large multimodal models can fill that gap: ClassComet reads a video's frames and transcript, creates six virtual learner personas, and generates danmaku of seven types, split into content-related (Q&A, discussion, highlights, summaries) and emotion-related (personal expressions, compliments, encouragement). In a 12-participant within-subjects study, videos carrying both content- and emotion-related generated danmaku produced significantly larger pre-to-post quiz gains and higher behavioral, emotional, and cognitive engagement than the same videos with no danmaku. A second comparison found the generated comments at least comparable to human-created danmaku on relevance, consistency, and fluency, with coherence still lower. The authors conclude that every lecture video, not just popular ones, could receive a helpful, encouraging live comment stream.

What carries the argument

The machine that carries the argument is a four-step LMM pipeline. Scene detection and frame sampling turn a long video into clip-level visual descriptions; an automatic transcription service produces timestamped text-level descriptions; a persona-creation prompt produces six distinct virtual viewers with backgrounds, personalities, and comment styles; and a structured prompt with in-context examples and explicit constraints (comments at most 12 characters, no gaps longer than 30 seconds, 15–25 content and 5–10 emotion comments per minute, more than 10 highlights per minute) generates the final danmaku stream. The seven-type taxonomy separates content-related danmaku (Q&A, discussion, highlights, summary) from emotion-related danmaku (personal expression, compliment, encouragement), and the persona layer converts isolated comments into simulated peer exchanges such as answers, praise, and encouragement.

What would settle it

A reanalysis of the stage-one data that breaks learning gains down by video would settle it: if the Content+Emotion advantage appears only in one video and disappears in the other three, the effect is video-driven rather than danmaku-driven. A preregistered replication with more participants and all four videos fully crossed with all four conditions would give the same answer more cleanly.

Watch

Extended reading notes

Core claim

The central discovery is that a multimodal model with access to both visual and textual information can generate the social layer of an educational video, and that this layer has measurable pedagogical value. The paper establishes a taxonomy of seven valued danmaku types in two classes and shows the classes have distinct effects: content-related danmaku alone improved quiz gains, behavioral engagement, and cognitive engagement; emotion-related danmaku alone improved emotional engagement; and only the combined condition improved all three engagement dimensions at once. Against human danmaku from the same video segments, the generated comments were rated higher on relevance and comparable on factual consistency and linguistic fluency, while human comments retained an edge in conversational coherence. The conclusion is that AI-generated danmaku can stand in for scarce user-generated danmaku without sacrificing quality on the dimensions that matter most for learning.

Load-bearing premise

The load-bearing premise is that the four videos were equivalent enough, after random pairing with the four danmaku conditions, for differences in quiz gains and engagement to be attributed to the danmaku rather than to video topic, difficulty, or participants' prior knowledge.

Editorial extensions

If this is right

  • Educational video platforms can offer a guaranteed danmaku layer on every video, including newly uploaded or low-view content where human comments are scarce.
  • Content- and emotion-related danmaku are complementary: content-only improves quiz gains and cognitive and behavioral engagement, emotion-only improves emotional engagement, and their combination is the only condition that moves all dimensions at once.
  • AI-generated danmaku can coexist with user-generated danmaku in the same stream, so platforms do not have to choose between automated and human comments.
  • The coherence gap in generated danmaku marks a concrete target: multi-turn interactions still read as less natural than human conversations, and improving that dimension is the clearest next step for the generator.
  • Future versions can vary the knowledge level of virtual personas, including less-expert teachable personas, to reduce the pressure some learners feel when generated answers arrive too quickly.

Reading between the lines

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

  • I infer that the active ingredient may be simulated social presence: if the learning gains are real, generated danmaku functions as a scalable substitute for watching with a real peer group, and would be worth comparing directly with other social-presence video augmentations.
  • The persona layer is a variable the paper does not isolate; I infer that a follow-up comparing persona-driven danmaku with the same text presented without named identities would reveal how much of the effect comes from believable virtual commenters.
  • Given the small sample and random video-condition pairing, I would not treat the effect sizes as portable until a replication crosses all four videos with all four conditions and checks whether any single topic drives the result.
  • I infer that adapting generation in real time—thinning content comments when a learner appears to be struggling, or thickening encouragement near difficult sections—is a natural extension the current pre-generated pipeline does not yet support.
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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

3 major / 4 minor

Summary. This paper presents ClassComet, a platform that uses large multimodal models (LMMs) to automatically generate danmaku (live synchronized video comments) for educational videos. The authors first run a formative study with seven participants to identify valued characteristics, resulting in seven danmaku types split into content-related (Q&A, discussion, highlights, summary) and emotion-related (personal emotion expression, brief compliment, encouragement). They then describe a generation pipeline built on GPT-4o that combines clip-level and text-level video descriptions with six virtual personas and a structured prompt template. The evaluation has two stages: a within-subjects study with 12 participants comparing four conditions (No-Danmaku, Emotion-only, Content-only, Content+Emotion) on learning gain and three engagement dimensions, and a quality comparison of generated danmaku versus human danmaku from Bilibili on relevancy, consistency, fluency, and coherence. The paper concludes that generated danmaku is comparable to human-created danmaku and that Content+Emotion danmaku significantly improves engagement and learning outcomes.

Significance. If the central claims were fully supported, this would be a useful contribution to HCI and educational technology: it provides a concrete design space for AI-generated social annotations and demonstrates a feasible pipeline for supplying educational videos with danmaku when human-generated comments are scarce. The work has notable strengths: the formative study grounds the seven-type taxonomy in user preferences; the pipeline is described in sufficient detail to be replicated, including the prompt templates in appendix C; and the quality comparison uses an external human benchmark from Bilibili, which is the right kind of reference point for the claim. The qualitative data enrich the quantitative results. However, the statistical evidence for the headline claims is currently incomplete and internally inconsistent, so the significance cannot be fully assessed until the analyses are corrected and reported in full.

major comments (3)
  1. [7.1.1–7.1.4] The reported ANOVA degrees of freedom are inconsistent with the stated design. For a one-way repeated-measures ANOVA with four conditions and N=12, the expected F-statistic has degrees of freedom (3,33) before sphericity correction, not F(4,12). The same F(4,12) pattern appears for learning outcome, behavioral engagement, emotional engagement, and cognitive engagement. Since the abstract's claims of significant improvement rest on these tests, the authors must provide the full ANOVA results, including corrected degrees of freedom if sphericity was adjusted, or a precise description of the actual model fitted. As written, the reported statistics cannot be evaluated.
  2. [6.2.2 and 7.1] The design pairs danmaku condition with video content within each participant, and the reported analyses do not control for this pairing. Each participant watched four videos (supervised learning, brain structure, music theory, and Latin alphabets) with one of the four danmaku conditions randomly paired per participant. With only 12 participants, random assignment does not ensure that video difficulty, topic interest, or prior knowledge is balanced across conditions, and the paper reports no video main effect, no condition-by-video interaction, and no balance table. The significant learning-outcome and engagement differences could therefore be driven by the specific videos rather than by the danmaku condition. The authors should add video as a factor in the model, or provide a convincing balance analysis and supplementary within-video comparisons.
  3. [7.1.5 and Figure 9] The quality comparison reports no inferential statistics. The text states that generated danmaku was 'superior' in relevancy, 'similar' in consistency and fluency, and 'lower' but 'almost comparable' in coherence, but no tests, effect sizes, or confidence intervals are reported for these comparisons. The abstract's claim that generated danmaku is 'comparable to human-created ones' is a headline result and needs formal support, for example paired comparisons that account for the six clips and the 12 raters, with appropriate multiple-comparison corrections.
minor comments (4)
  1. [5.3.3 and Appendix C] The prompt template specifies '15-25 content-related danmaku and 5-10 emotion-related danmaku per minute' and 'more than 10 highlight per minute'; these numbers seem high relative to the earlier statement that danmaku frequency in educational videos ranges from 2 to 30 per minute, and the paper does not report how the generated output was validated against these constraints. Please clarify the intended counts and describe any post-generation filtering or verification.
  2. [4.2] The usage scenario refers to 'Figure 5-A' and 'Figure 5-C' when describing the video player controls and danmaku input box, but the relevant interface figure is Figure 2. Please correct these cross-references.
  3. [Appendices A–C] Several appendix passages contain garbled characters and artifacts, such as 'texcl' and 'âĂŹ', which should be cleaned before publication.
  4. [6.2.3 and 7.1.1] The learning-outcome measure is the post-quiz minus pre-quiz score, but the paper does not report the pre-quiz scores or check for ceiling or floor effects. Please include descriptive statistics for both pre and post scores for each condition.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's central claims are tested against external human-created danmaku and independent pre/post quizzes, with no self-citation chain or definitional reduction of a prediction to its inputs.

full rationale

I walked the claimed derivation chain: (1) the formative study derives valued danmaku characteristics from participant interviews; (2) these characteristics are then used as design specifications for the ClassComet generation pipeline; (3) the learning-outcome and engagement claims are tested in a within-subjects study with pre/post quizzes and engagement questionnaires; (4) the quality claim is tested by comparing generated danmaku against human-created Bilibili danmaku on an external benchmark. None of these steps reduces to its inputs by construction. The seven danmaku types are the independent variable, not a fitted parameter masquerading as a prediction: the study tests whether adding those types changes learning outcomes, and the comparison to human danmaku is an external, falsifiable benchmark. The paper contains no self-citations that are load-bearing, no uniqueness theorem imported from the authors' prior work, and no ansatz smuggled in via citation. The frequency and length constraints in the prompt (e.g., '15-25 content-related danmaku and 5-10 emotion-related danmaku per minute') are design parameters, and later qualitative praise of posting frequency is a user reaction to that design, not a prediction derived from the same fitted values. The reported ANOVA degrees of freedom (F(4,12)) appear inconsistent with a one-way repeated-measures design with four conditions and twelve participants, and the condition-video random assignment with n=12 may leave video difficulty unbalanced; however, these are statistical validity and confounding concerns, not circularity, and per the review rules they do not count as a circularity finding. The central claims therefore rest on independent empirical evaluation rather than on a definitional or self-referential chain.

Assumptions & free parameters 5 free parameters · 4 assumptions · 1 invented entities

The central claim rests on design constants for danmaku quantity and length, on the assumption that quiz gains reflect learning, and on the reliability of GPT-4o's video understanding. No data, code, or model outputs are shared.

free parameters (5)
  • content danmaku target count = 15-25 per minute
    Set in the prompt to match prior danmaku frequency measurements; not fitted to outcome data.
  • emotion danmaku target count = 5-10 per minute
    Set to keep emotion danmaku less frequent than content danmaku, following prior literature.
  • highlight danmaku minimum = more than 10 per minute
    Chosen because prior work reported highlighted danmaku make up over 50% of content-related danmaku.
  • maximum danmaku length = 12 characters
    Based on average danmaku length from prior work.
  • number of virtual personas = 6
    Chosen to match the observed posting frequency of about 25-30 danmaku per minute in educational videos.
assumptions (4)
  • standard math Repeated-measures ANOVA and Tukey HSD are appropriate for the engagement and learning-outcome data.
    The analysis in Section 7.1 assumes normality and sphericity of difference scores; no corrections (e.g., Greenhouse-Geisser) are reported despite the inconsistent df.
  • domain assumption Self-reported engagement questionnaires (7-point Likert) measure behavioral, emotional, and cognitive engagement.
    Section 6.2.3 adapts six existing scales; the paper assumes these self-reports are valid proxies for engagement constructs.
  • domain assumption Post-minus-pre quiz difference scores measure learning outcome.
    Section 6.2.3 uses the difference as the learning measure; it assumes the quizzes are sensitive, non-ceiling, and that gains are not just exposure to danmaku-provided answers.
  • domain assumption GPT-4o-produced video descriptions and danmaku are accurate and on-topic as used.
    The whole pipeline in Section 5 assumes the LMM reliably converts sampled frames and transcripts into clip descriptions and then into danmaku that are factually consistent with the video.
invented entities (1)
  • virtual personas
    purpose: Simulate diverse viewer interactions (Q&A, discussion, encouragement) so generated danmaku feels social and covers all video sections.
    Personas are a design component, not an independently verified entity; their contribution is entangled with the prompt and the LMM, and the paper provides no isolated validation of personas.

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Cite this review

Pith. "Pith review of ClassComet: Exploring and Designing AI-generated Danmaku in Educational Videos to Enhance Online Learning." pith.science (2026). https://pith.science/paper/R6DFG2DB

@misc{pith2026250418189,
  author       = {Pith},
  title        = {Pith review of: ClassComet: Exploring and Designing AI-generated Danmaku in Educational Videos to Enhance Online Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/R6DFG2DB}},
  note         = {Machine review of arXiv:2504.18189}
}
read the original abstract

Danmaku, users' live comments synchronized with, and overlaying on videos, has recently shown potential in promoting online video-based learning. However, user-generated danmaku can be scarce-especially in newer or less viewed videos and its quality is unpredictable, limiting its educational impact. This paper explores how large multimodal models (LMM) can be leveraged to automatically generate effective, high-quality danmaku. We first conducted a formative study to identify the desirable characteristics of content- and emotion-related danmaku in educational videos. Based on the obtained insights, we developed ClassComet, an educational video platform with novel LMM-driven techniques for generating relevant types of danmaku to enhance video-based learning. Through user studies, we examined the quality of generated danmaku and their influence on learning experiences. The results indicate that our generated danmaku is comparable to human-created ones, and videos with both content- and emotion-related danmaku showed significant improvement in viewers' engagement and learning outcome.

Figures

Figures reproduced from arXiv: 2504.18189 by the authors.

Figure 1
Figure 1. A sample screenshot of an educational video with danmaku. The translations of the displayed danmaku are: 1. “I didn’t linked [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. User interface of ClassComet: (A) video control buttons for play/pause, volume adjustment, and speed control, (B) danmaku [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Examples of different types of danmaku (including both content-related and emotion-related) generated by ClassComet in [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: The ClassComet pipeline for automatically generating danmaku in educational videos includes four steps: (A) Extract video [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]
Figure 5
Figure 5. Figure 5: The structured prompt template of generating danmaku which consists of the system and user prompts as well as the pass [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]
Figure 6
Figure 6. Figure 6: Diagram of the experimental procedure, consisting of two stages: (S1) evaluating the impact of different danmaku on viewers’ [PITH_FULL_IMAGE:figures/full_fig_p015_6.png]
Figure 7
Figure 7. Figure 7: Box plot of learning outcome for different conditions. Error bars show 95% confidence intervals. Asterisk ( [PITH_FULL_IMAGE:figures/full_fig_p018_7.png]
Figure 8
Figure 8. Figure 8: Box plots of behavioral engagement, emotional engagement, and cognitive engagement for different conditions. Error bars [PITH_FULL_IMAGE:figures/full_fig_p020_8.png]
Figure 9
Figure 9. Figure 9: Means of danmaku quality score assessed by relevancy, consistency, fluency, and coherence. [PITH_FULL_IMAGE:figures/full_fig_p020_9.png]

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Reference graph

Works this paper leans on

259 extracted references · 49 canonical work pages

  1. [1]

    Hervé Abdi and Lynne J Williams. 2010. TukeyâĂŹs honestly significant difference (HSD) test. Encyclopedia of research design 3, 1 (2010), 1–5

    2010

  2. [2]

    Xin An, Xue Gong, Rongchi Zhao, Qiuyu Chen, and Ning Ma. 2020. A comparative study of the interaction features of barrage and forum in social learning course. International Journal of Information and Education Technology 10, 5 (2020), 383–388

    2020

  3. [3]

    Darst Ang Chen and Robert P

    Paul W. Darst Ang Chen and Robert P. Pangrazi. 1999. What Constitutes Situational Interest? Validating a Construct in Phys- ical Education. Measurement in Physical Education and Exercise Science 3, 3 (1999), 157–XXX. doi:10.1207/s15327841mpee0303_3 arXiv:https://doi.org/10.1207/s15327841mpee0303_3

    work page doi:10.1207/s15327841mpee0303_3 1999

  4. [4]

    Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. 2015. Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision . 2425–2433

    2015

  5. [5]

    Arthur N Applebee and Judith A Langer. 1983. Instructional scaffolding: Reading and writing as natural language activities. Language arts 60, 2 (1983), 168–175

    1983

  6. [6]

    Isabelle Archambault, Michel Janosz, Jean-Sébastien Fallu, and Linda S Pagani. 2009. Student engagement and its relationship with early high school dropout. Journal of adolescence 32, 3 (2009), 651–670

    2009

  7. [7]

    Fawad Baig

    Dr. Fawad Baig. 2022. The Impact of Educational Videos on the Academic Performance of University Students in Distance Learning. Journal of Positive School Psychology 6 (12 2022), 1233–1249

    2022

  8. [8]

    Zohar Barnett-Itzhaki, Dizza Beimel, and Arava Tsoury. 2023. Using a Variety of Interactive Learning Methods to Improve Learning Effectiveness: Insights from AI Models Based on Teaching Surveys. Online Learning 27 (09 2023). doi:10.24059/olj.v27i3.3575

    work page doi:10.24059/olj.v27i3.3575 2023

Show all 259 references
  1. [9]

    William Cai, Josh Grossman, Zhiyuan Jerry Lin, Hao Sheng, Johnny Tian-Zheng Wei, Joseph Jay Williams, and Sharad Goel. 2021. Bandit algorithms to personalize educational chatbots. Machine Learning 110, 9 (2021), 2389–2418

    2021

  2. [10]

    Maximillian Chen, Alexandros Papangelis, Chenyang Tao, Andy Rosenbaum, Seokhwan Kim, Yang Liu, Zhou Yu, and Dilek Hakkani-Tur. 2022. Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding. arXiv:2210.14169 [cs.CL] https://arxiv.org/abs/2210.14169

    2022 arXiv

  3. [11]

    Yue Chen, Qin Gao, and Ge Gao. 2022. Timeline-anchored comments in video-based learning: The impact of visual layout and content depth. International Journal of Human–Computer Interaction 38, 9 (2022), 868–883

    2022

  4. [12]

    Yue Chen, Qin Gao, Quan Yuan, and Yuanli Tang. 2019. Facilitating studentsâĂŹ interaction in MOOCs through timeline-anchored discussion. International Journal of Human–Computer Interaction 35, 19 (2019), 1781–1799

    2019

  5. [13]

    Zhilong Chen, Hancheng Cao, Yuting Deng, Xuan Gao, Jinghua Piao, Fengli Xu, Yu Zhang, and Yong Li. 2021. Learning from home: A mixed-methods analysis of live streaming based remote education experience in chinese colleges during the covid-19 pandemic. In Proceedings of the 202...

    2021

  6. [14]

    Pei-Yu Chi, Joyce Liu, Jason Linder, Mira Dontcheva, Wilmot Li, and Bjoern Hartmann. 2013. DemoCut: generating concise instructional videos for physical demonstrations. In Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology (St. Andrews, Scot...

    2013

  7. [15]

    Ruiqi Deng, Pierre Benckendorff, and Deanne Gannaway. 2020. Learner engagement in MOOCs: Scale development and validation. British Journal of Educational Technology 51, 1 (2020), 245–262

    2020

  8. [16]

    M Dewan, Mahbub Murshed, and Fuhua Lin. 2019. Engagement detection in online learning: a review. Smart Learning Environments 6, 1 (2019), 1–20

    2019

  9. [17]

    Jeffrey Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, and Trevor Darrell. 2015. Long-term recurrent convolutional networks for visual recognition and description. In Proceedings of the IEEE conference on computer vision ...

    2015

  10. [18]

    Hao Fang, Saurabh Gupta, Forrest Iandola, Rupesh K Srivastava, Li Deng, Piotr Dollár, Jianfeng Gao, Xiaodong He, Margaret Mitchell, John C Platt, et al. 2015. From captions to visual concepts and back. In Proceedings of the IEEE conference on computer vision and pattern recogn...

    2015

  11. [19]

    Daniel Fernández Galeote, Nikoletta-Zampeta Legaki, and Juho Hamari. 2023. From Traditional to Game-Based Learning of Climate Change: A Media Comparison Experiment. Proceedings of the ACM on Human-Computer Interaction 7, CHI PLAY (2023), 503–525

    2023

  12. [20]

    JD Finn. 1993. School Engagement & Students At Risk. (1993)

    1993

  13. [21]

    Jeremy D Finn, Gina M Pannozzo, and Kristin E Voelkl. 1995. Disruptive and inattentive-withdrawn behavior and achievement among fourth graders. The Elementary School Journal 95, 5 (1995), 421–434

    1995

  14. [22]

    Jeremy D Finn and Donald A Rock. 1997. Academic success among students at risk for school failure. Journal of applied psychology 82, 2 (1997), 221

    1997

  15. [23]

    Jeremy D Finn and Kayla S Zimmer. 2012. Student engagement: What is it? Why does it matter? In Handbook of research on student engagement . Springer, 97–131

    2012

  16. [24]

    Jennifer A Fredricks, Phyllis C Blumenfeld, and Alison H Paris. 2004. School engagement: Potential of the concept, state of the evidence. Review of educational research 74, 1 (2004), 59–109

    2004

  17. [25]

    Michail Giannakos, Konstantinos Chorianopoulos, Marco Ronchetti, Peter Szegedi, and Stephanie Teasley. 2014. Video-based learning and open online courses. (2014)

    2014

  18. [26]

    Barbara A Greene. 2015. Measuring cognitive engagement with self-report scales: Reflections from over 20 years of research. Educational Psychologist 50, 1 (2015), 14–30

    2015

  19. [27]

    Philip J Guo, Juho Kim, and Rob Rubin. 2014. How video production affects student engagement: An empirical study of MOOC videos. InProceedings of the first ACM conference on Learning@ scale conference . 41–50

    2014

  20. [28]

    Jeong-Won Han, Junhee Park, and Hanna Lee. 2022. Analysis of the effect of an artificial intelligence chatbot educational program on non-face-to-face classes: a quasi-experimental study. BMC Medical Education 22, 1 (2022), 830

    2022

  21. [29]

    Bo He, Hengduo Li, Young Kyun Jang, Menglin Jia, Xuefei Cao, Ashish Shah, Abhinav Shrivastava, and Ser-Nam Lim. 2024. MA-LMM: Memory- Augmented Large Multimodal Model for Long-Term Video Understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Re...

    2024

  22. [30]

    Bo He, Hengduo Li, Young Kyun Jang, Menglin Jia, Xuefei Cao, Ashish Shah, Abhinav Shrivastava, and Ser-Nam Lim. 2024. MA-LMM: Memory- Augmented Large Multimodal Model for Long-Term Video Understanding. arXiv:2404.05726 [cs.CV] https://arxiv.org/abs/2404.05726

    2024 arXiv

  23. [31]

    Changyang He, Lu He, Tun Lu, and Bo Li. 2021. Beyond Entertainment: Unpacking Danmaku and Comments’ Role of Information Sharing and Sentiment Expression in Online Crisis Videos. Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021), 1–27

    2021

  24. [32]

    Jia He, Mukund Rungta, David Koleczek, Arshdeep Sekhon, Franklin X Wang, and Sadid Hasan. 2024. Does Prompt Formatting Have Any Impact on LLM Performance? arXiv:2411.10541 [cs.CL] https://arxiv.org/abs/2411.10541

    2024 arXiv

  25. [34]

    Siying Hu and Zhicong Lu. 2024. Exploring the Danmaku Content Moderation on Video-Sharing Platforms: Existing Limitations, Challenges, and Design Opportunities. arXiv:2411.04529 [cs.HC] https://arxiv.org/abs/2411.04529

    2024 arXiv

  26. [35]

    Siying Hu, Huanchen Wang, Yu Zhang, Piaohong Wang, and Zhicong Lu. 2024. DanModCap: Designing a Danmaku Moderation Tool for Video-Sharing Platforms that Leverages Impact Captions. arXiv:2408.02574 [cs.HC] https://arxiv.org/abs/2408.02574

    2024 arXiv

  27. [36]

    Zeyu Huang, Xinyi Cao, Yuanhao Zhang, and Xiaojuan Ma. 2024. Sharing Frissons among Online Video Viewers: Exploring the Design of Affective Communication for Aesthetic Chills. In Proceedings of the CHI Conference on Human Factors in Computing Systems . 1–19

    2024

  28. [37]

    Yu-Kai Hung, Yun-Chien Huang, Ting-Yu Su, Yen-Ting Lin, Lung-Pan Cheng, Bryan Wang, and Shao-Hua Sun. 2024. SimTube: Generating Simulated Video Comments through Multimodal AI and User Personas. arXiv preprint arXiv:2411.09577 (2024)

    2024 arXiv

  29. [38]

    Md Mohaiminul Islam, Ngan Ho, Xitong Yang, Tushar Nagarajan, Lorenzo Torresani, and Gedas Bertasius. 2024. Video ReCap: Recursive Captioning of Hour-Long Videos. arXiv:2402.13250 [cs.CV] https://arxiv.org/abs/2402.13250

    2024 arXiv

  30. [39]

    Qiao Jin, Yu Liu, Ruixuan Sun, Chen Chen, Puqi Zhou, Bo Han, Feng Qian, and Svetlana Yarosh. 2023. Collaborative online learning with vr video: Roles of collaborative tools and shared video control. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems . 1–18

    2023

  31. [40]

    Kumara Kahatapitiya, Kanchana Ranasinghe, Jongwoo Park, and Michael S. Ryoo. 2024. Language Repository for Long Video Understanding. arXiv:2403.14622 [cs.CV] https://arxiv.org/abs/2403.14622

    2024 arXiv

  32. [41]

    Andrej Karpathy and Li Fei-Fei. 2015. Deep visual-semantic alignments for generating image descriptions. In Proceedings of the IEEE conference on computer vision and pattern recognition . 3128–3137

    2015

  33. [42]

    Hyunwoo Kim, Jack Hessel, Liwei Jiang, Peter West, Ximing Lu, Youngjae Yu, Pei Zhou, Ronan Le Bras, Malihe Alikhani, Gunhee Kim, Maarten Sap, and Yejin Choi. 2023. SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization. arXiv:2212.10465 [cs.CL] htt...

    2023 arXiv

  34. [43]

    Junseok Kim, Nakyeong Yang, and Kyomin Jung. 2024. Persona is a Double-edged Sword: Mitigating the Negative Impact of Role-playing Prompts in Zero-shot Reasoning Tasks. arXiv:2408.08631 [cs.CL] https://arxiv.org/abs/2408.08631

    2024 arXiv

  35. [44]

    Seoyoung Kim, Donghoon Shin, Jeongyeon Kim, Soonwoo Kwon, and Juho Kim. 2023. How Older Adults Use Online Videos for Learning. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems . 1–16

    2023

  36. [45]

    Wojciech KryÅŻciÅĎski, Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, and Richard Socher. 2019. Neural Text Summarization: A Critical Evaluation. arXiv:1908.08960 [cs.CL] https://arxiv.org/abs/1908.08960 ClassComet 27

    2019 arXiv

  37. [46]

    Lasha Labadze, Maya Grigolia, and Lela Machaidze. 2023. Role of AI chatbots in education: systematic literature review. International Journal of Educational Technology in Higher Education 20, 1 (2023), 56

    2023

  38. [47]

    Ken Jen Lee, Apoorva Chauhan, Joslin Goh, Elizabeth Nilsen, and Edith Law. 2021. Curiosity notebook: the design of a research platform for learning by teaching. Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021), 1–26

    2021

  39. [48]

    Feng Li, Renrui Zhang, Hao Zhang, Yuanhan Zhang, Bo Li, Wei Li, Zejun Ma, and Chunyuan Li. 2024. LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models. arXiv:2407.07895 [cs.CV] https://arxiv.org/abs/2407.07895

    2024 arXiv

  40. [49]

    KunChang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao. 2024. VideoChat: Chat-Centric Video Understanding. arXiv:2305.06355 [cs.CV] https://arxiv.org/abs/2305.06355

    2024 arXiv

  41. [50]

    Yu Li, Shang Qu, Jili Shen, Shangchao Min, and Zhou Yu. 2024. Curriculum-Driven Edubot: A Framework for Developing Language Learning Chatbots Through Synthesizing Conversational Data. arXiv:2309.16804 [cs.CL] https://arxiv.org/abs/2309.16804

    2024 arXiv

  42. [51]

    Zhikai Li. 2022. Influence of online learning behavior and video playing questions on studentsâĂŹ learning effect. International Journal of Emerging Technologies in Learning (iJET) 17, 2 (2022), 223–238

    2022

  43. [52]

    Kevin Lin, Faisal Ahmed, Linjie Li, Chung-Ching Lin, Ehsan Azarnasab, Zhengyuan Yang, Jianfeng Wang, Lin Liang, Zicheng Liu, Yumao Lu, et al

  44. [53]

    Elizabeth A Linnenbrink and Paul R Pintrich. 2003. The role of self-efficacy beliefs instudent engagement and learning intheclassroom. Reading &Writing Quarterly 19, 2 (2003), 119–137

    2003

  45. [54]

    Fuxiao Liu, Xiaoyang Wang, Wenlin Yao, Jianshu Chen, Kaiqiang Song, Sangwoo Cho, Yaser Yacoob, and Dong Yu. 2024. MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning. arXiv:2311.10774 [cs.CL] https://arxiv.org/abs/2311.10774

    2024 arXiv

  46. [55]

    Shuming Ma, Lei Cui, Damai Dai, Furu Wei, and Xu Sun. 2018. LiveBot: Generating Live Video Comments Based on Visual and Textual Contexts. arXiv:1809.04938 [cs.CL] https://arxiv.org/abs/1809.04938

    2018 arXiv

  47. [56]

    Wenjuan MA, Wenjing MA, Yongbin HU, and Xinyu BI. 2024. The who, why, and how of ai-based chatbots for learning and teaching in higher education: A systematic review. Education and Information Technologies (2024), 1–25

    2024

  48. [57]

    Xiaojuan Ma and Nan Cao. 2017. Video-based evanescent, anonymous, asynchronous social interaction: Motivation and adaption to medium. In Proceedings of the 2017 ACM conference on computer supported cooperative work and social computing . 770–782

    2017

  49. [58]

    Noboru Matsuda, Evelyn Yarzebinski, Victoria Keiser, Rohan Raizada, William W Cohen, Gabriel J Stylianides, and Kenneth R Koedinger. 2013. Cognitive anatomy of tutor learning: Lessons learned with SimStudent. Journal of Educational Psychology 105, 4 (2013), 1152

    2013

  50. [59]

    Richard E Mayer. 2014. Incorporating motivation into multimedia learning. Learning and instruction 29 (2014), 171–173

    2014

  51. [60]

    Mariani Mohd Dahlan, Nuri Halim, Noor Kamarudin, and Fatanah Ahmad. 2023. Exploring interactive video learning: Techniques, applications, and pedagogical insights. International Journal of ADV ANCED AND APPLIED SCIENCES 10 (12 2023), 220–230. doi:10.21833/ijaas.2023.12.024

    2023 doi

  52. [61]

    Ya Mou, Bin Jing, Yichun Li, Nanyang Fang, and Changcheng Wu. 2022. Interactivity in learning instructional videos: Sending danmaku improved parasocial interaction but reduced learning performance. Frontiers in Psychology 13 (2022), 1066164

    2022

  53. [62]

    Aliakbar Nafar, Kristen Brent Venable, and Parisa Kordjamshidi. 2024. Learning vs Retrieval: The Role of In-Context Examples in Regression with LLMs. arXiv:2409.04318 [cs.CL] https://arxiv.org/abs/2409.04318

    2024 arXiv

  54. [63]

    Jiao Ou, Junda Lu, Che Liu, Yihong Tang, Fuzheng Zhang, Di Zhang, and Kun Gai. 2024. DialogBench: Evaluating LLMs as Human-like Dialogue Systems. arXiv:2311.01677 [cs.CL] https://arxiv.org/abs/2311.01677

    2024 arXiv

  55. [64]

    Joon Sung Park, Lindsay Popowski, Carrie Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. 2022. Social simulacra: Creating populated prototypes for social computing systems. In Proceedings of the 35th Annual ACM Symposium on User Interface Software and Techno...

    2022

  56. [65]

    Heinrich Peters, Moran Cerf, and Sandra C. Matz. 2024. Large Language Models Can Infer Personality from Free-Form User Interactions. arXiv:2405.13052 [cs.HC] https://arxiv.org/abs/2405.13052

    2024 arXiv

  57. [66]

    Ludovica Piro, Tommaso Bianchi, Luca Alessandrelli, Andrea Chizzola, Daniela Casiraghi, Susanna Sancassani, and Nicola Gatti. 2024. MyLearn- ingTalk: An LLM-Based Intelligent Tutoring System. In International Conference on Web Engineering . Springer, 428–431

    2024

  58. [67]

    Long Qian, Juncheng Li, Yu Wu, Yaobo Ye, Hao Fei, Tat-Seng Chua, Yueting Zhuang, and Siliang Tang. 2024. Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning. arXiv:2402.11435 [cs.CV] https://arxiv.org/abs/2402.11435

    2024 arXiv

  59. [68]

    Shuhuai Ren, Linli Yao, Shicheng Li, Xu Sun, and Lu Hou. 2024. TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding. arXiv:2312.02051 [cs.CV] https://arxiv.org/abs/2312.02051

    2024 arXiv

  60. [69]

    Yuchen Ren, Yuan Yuan, and Lei Chen. 2022. Multi-modal guided attention for live video comments generation. In International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021) , Vol. 12168. SPIE, 267–273

    2022

  61. [70]

    Anna Rohrbach, Marcus Rohrbach, Wei Qiu, Annemarie Friedrich, Manfred Pinkal, and Bernt Schiele. 2014. Coherent multi-sentence video description with variable level of detail. In Pattern Recognition: 36th German Conference, GCPR 2014, Münster, Germany, September 2-5, 2014, Pro...

    2014

  62. [71]

    Marija Sablić, Ana Mirosavljević, and Alma Škugor. 2021. Video-based learning (VBL)âĂŤpast, present and future: An overview of the research published from 2008 to 2019. Technology, Knowledge and Learning 26, 4 (2021), 1061–1077

    2021

  63. [72]

    Harandi, Nathan Roberson, Sidney Fels, and Ido Roll

    Kyoungwon Seo, Samuel Dodson, Negar M. Harandi, Nathan Roberson, Sidney Fels, and Ido Roll. 2021. Active learning with online video: The impact of learning context on engagement. Computers and Education 165 (2021), 104132. doi:10.1016/j.compedu.2021.104132 28 Zipeng et al

    2021

  64. [73]

    Ellen Skinner, Carrie Furrer, Gwen Marchand, and Thomas Kindermann. 2008. Engagement and disaffection in the classroom: Part of a larger motivational dynamic? Journal of educational psychology 100, 4 (2008), 765

    2008

  65. [74]

    Ellen A Skinner, Thomas A Kindermann, and Carrie J Furrer. 2009. A motivational perspective on engagement and disaffection: Conceptualization and assessment of children’s behavioral and emotional participation in academic activities in the classroom. Educational and psychologi...

    2009

  66. [75]

    Enxin Song, Wenhao Chai, Guanhong Wang, Yucheng Zhang, Haoyang Zhou, Feiyang Wu, Haozhe Chi, Xun Guo, Tian Ye, Yanting Zhang, et al

  67. [76]

    Enxin Song, Wenhao Chai, Guanhong Wang, Yucheng Zhang, Haoyang Zhou, Feiyang Wu, Haozhe Chi, Xun Guo, Tian Ye, Yanting Zhang, Yan Lu, Jenq-Neng Hwang, and Gaoang Wang. 2024. MovieChat: From Dense Token to Sparse Memory for Long Video Understanding. arXiv:2307.16449 [cs.CV] htt...

    2024 arXiv

  68. [77]

    Songxin Tan and Zixing Shen. 2024. Relationship Between Cognitive Features and Social Media Engagement: An Analysis of YouTube Science Videos. IEEE Transactions on Engineering Management 71 (2024), 10116–10125. doi:10.1109/TEM.2023.3330677

    2024

  69. [78]

    Thitaree Tanprasert, Sidney S Fels, Luanne Sinnamon, and Dongwook Yoon. 2023. Scripted vicarious dialogues: Educational video augmentation method for increasing isolated studentsâĂŹ engagement. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems . 1–25

    2023

  70. [79]

    Kati Vasalampi, Joona Muotka, Sanni Pöysä, Marja-Kristiina Lerkkanen, Anna-Maija Poikkeus, and Jari-Erik Nurmi. 2016. Assessment of students’ situation-specific classroom engagement by an InSitu Instrument. Learning and Individual Differences 52 (2016), 46–52

    2016

  71. [80]

    Subhashini Venugopalan, Marcus Rohrbach, Jeffrey Donahue, Raymond Mooney, Trevor Darrell, and Kate Saenko. 2015. Sequence to sequence-video to text. In Proceedings of the IEEE international conference on computer vision . 4534–4542

    2015

  72. [81]

    Jiaqi Wang, Hua Shen, Chacha Chen, and Frank Ritter. 2021. Are Learners Satisfied with Their MOOC Experiences? Assessing and Improving Online LearnersâĂŹ Interactions. In Proceedings of the Asian CHI Symposium 2021 . 215–220

    2021

  73. [82]

    Weiying Wang, Jieting Chen, and Qin Jin. 2020. Videoic: A video interactive comments dataset and multimodal multitask learning for comments generation. In Proceedings of the 28th ACM International Conference on Multimedia . 2599–2607

    2020

  74. [83]

    Xiaohan Wang, Yuhui Zhang, Orr Zohar, and Serena Yeung-Levy. 2024. VideoAgent: Long-form Video Understanding with Large Language Model as Agent. arXiv:2403.10517 [cs.CV] https://arxiv.org/abs/2403.10517

    2024 arXiv

  75. [84]

    Yujia Wang, Wei Liang, Haikun Huang, Yongqi Zhang, Dingzeyu Li, and Lap-Fai Yu. 2021. Toward automatic audio description generation for accessible videos. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems . 1–12

    2021

  76. [85]

    Ze Wang, Christi Bergin, and David A Bergin. 2014. Measuring engagement in fourth to twelfth grade classrooms: The Classroom Engagement Inventory. School Psychology Quarterly 29, 4 (2014), 517

    2014

  77. [86]

    Yuetian Weng, Mingfei Han, Haoyu He, Xiaojun Chang, and Bohan Zhuang. 2024. LongVLM: Efficient Long Video Understanding via Large Language Models. arXiv:2404.03384 [cs.CV] https://arxiv.org/abs/2404.03384

    2024 arXiv

  78. [87]

    Rainer Winkler, Sebastian Hobert, Antti Salovaara, Matthias Söllner, and Jan Marco Leimeister. 2020. Sara, the Lecturer: Improving Learning in Online Education with a Scaffolding-Based Conversational Agent. In Proceedings of the 2020 CHI Conference on Human Factors in Computin...

    2020

  79. [88]

    Hao Wu, François Pitié, and Gareth Jones. 2021. Cold start problem for automated live video comments. In Proceedings of the Third Workshop on Multimodal Artificial Intelligence. 54–62

    2021

  80. [89]

    Qunfang Wu, Yisi Sang, and Yun Huang. 2019. Danmaku: A new paradigm of social interaction via online videos. ACM Transactions on Social Computing 2, 2 (2019), 1–24

    2019

  81. [90]

    Qunfang Wu, Yisi Sang, Shan Zhang, and Yun Huang. 2018. Danmaku vs. forum comments: understanding user participation and knowledge sharing in online videos. In Proceedings of the 2018 ACM International Conference on Supporting Group Work . 209–218

    2018

  82. [91]

    Tongshuang Wu, Ellen Jiang, Aaron Donsbach, Jeff Gray, Alejandra Molina, Michael Terry, and Carrie J Cai. 2022. PromptChainer: Chaining Large Language Model Prompts through Visual Programming. arXiv:2203.06566 [cs.HC] https://arxiv.org/abs/2203.06566

    2022 arXiv

  83. [92]

    Ziang Xiao, Michelle X Zhou, and Wat-Tat Fu. 2019. Who should be my teammates: Using a conversational agent to understand individuals and help teaming. In Proceedings of the 24th international conference on intelligent user interfaces . 437–447

    2019

  84. [93]

    Lu Xu, Sijie Zhu, Chunyuan Li, Chia-Wen Kuo, Fan Chen, Xinyao Wang, Guang Chen, Dawei Du, Ye Yuan, and Longyin Wen. 2024. Beyond Raw Videos: Understanding Edited Videos with Large Multimodal Model. arXiv:2406.10484 [cs.CV] https://arxiv.org/abs/2406.10484

    2024 arXiv

  85. [94]

    Nicholas Yan. 2023. Generating Rhythm Game Music with Jukebox. arXiv:2401.01997 [cs.SD] https://arxiv.org/abs/2401.01997

    2023 arXiv

  86. [95]

    Bo Yang. 2024. Combining Danmaku and Discussion Boards: Toward A Scalable and Sociable Environment for Mass Collaboration in MOOCs. International Journal of Computer-Supported Collaborative Learning (2024), 1–29

    2024

  87. [96]

    Yaxing Yao, Jennifer Bort, and Yun Huang. 2017. Understanding Danmaku’s potential in online video learning. In Proceedings of the 2017 CHI conference extended abstracts on human factors in computing systems . 3034–3040

    2017

  88. [97]

    Quan Yuan and Qin Gao. 2024. Being there, and being together: Avatar appearance and peer interaction in VR classrooms for video-based learning. International Journal of Human–Computer Interaction 40, 13 (2024), 3313–3333

    2024

  89. [98]

    Min Zhang, Qiang Jiang, Weiyan Xiong, Qi Li, and Wei Zhao. 2024. Effects of real-time danmaku interaction on student engagement in live video-streaming teaching: analyzing the mediating roles of social, teaching, and cognitive presences. Interactive Learning Environments (2024...

    2024

  90. [99]

    Manman Zhang, Ge Luo, Yuchen Ma, Sheng Li, Zhenxing Qian, and Xinpeng Zhang. 2023. VCMaster: Generating Diverse and Fluent Live Video Comments Based on Multimodal Contexts. In Proceedings of the 31st ACM International Conference on Multimedia (Ottawa ON, Canada) (MM ’23). Asso...

    2023

  91. [100]

    Yu Zhang, Changyang He, Huanchen Wang, and Zhicong Lu. 2023. Understanding communication strategies and viewer engagement with science knowledge videos on bilibili. In Proceedings of the 2023 CHI conference on human factors in computing systems . 1–18

    2023

  92. [101]

    Yabo Zhang, Aiping Qian, Zhongling Pi, and Jiumin Yang. 2019. Danmaku related to video content facilitates learning. Journal of Educational Technology Systems 47, 3 (2019), 359–372

    2019

  93. [102]

    Zhihan Zhang, Zhiyi Yin, Shuhuai Ren, Xinhang Li, and Shicheng Li. 2020. Dca: Diversified co-attention towards informative live video commenting. In Natural Language Processing and Chinese Computing: 9th CCF International Conference, NLPCC 2020, Zhengzhou, China, October 14–18...

    2020

  94. [103]

    Qi Zhao, Shijie Wang, Ce Zhang, Changcheng Fu, Minh Quan Do, Nakul Agarwal, Kwonjoon Lee, and Chen Sun. 2024. AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos? arXiv:2307.16368 [cs.CV] https://arxiv.org/abs/2307.16368

    2024 arXiv

  95. [104]

    Rongchi Zhao, Qiuyue Chen, Xin An, Xueqi Gong, and Ning Ma. 2019. A Comparative Analysis of Forum and Barrage Interactive Patterns in Online Language Learning. 2019 Eighth International Conference on Educational Innovation through Technology (EITT) (2019), 25–30. https: //api....

    2019

  96. [105]

    Xing, Hao Zhang, Joseph E

    Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023. Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. arXiv:2306.05685 [cs.CL] https://ar...

    2023 arXiv

  97. [106]

    Wangchunshu Zhou, Yuchen Eleanor Jiang, Ryan Cotterell, and Mrinmaya Sachan. 2023. Efficient Prompting via Dynamic In-Context Learning. arXiv:2305.11170 [cs.CL] https://arxiv.org/abs/2305.11170

    2023 arXiv

  98. [107]

    Introduction to Psychology

    Novan Zulkarnain, Harjanto Prabowo, Ford Lumban Gaol, and Sani Muhamad Isa. 2023. Video Quality Indicators for Video-Based Learning System in Higher Education. In Proceedings of the 2023 9th International Conference on Frontiers of Educational Technologies (Bali, Indonesia) (I...

    2023

  99. [110]

    What’s Unsupervised Learning?

  100. [111]

    Learning from data without labels to find patterns

  101. [112]

    Learning with labeled data

  102. [113]

    Learning without any data

  103. [114]

    Learning with small datasets

  104. [115]

    What is a key part of unsupervised learning?

  105. [116]

    Reinforcement learning

  106. [117]

    What is NOT a goal of unsupervised learning?

  107. [118]

    Finding structures in data

  108. [119]

    Grouping data into clusters

  109. [120]

    36 Zipeng et al

    Providing a right answer for every input. 36 Zipeng et al

  110. [121]

    Which of the following is an application of unsupervised learning?

  111. [122]

    What is anomaly detection in unsupervised learning used for?

  112. [123]

    Grouping similar data points

  113. [124]

    Detecting unusual events

  114. [125]

    Solving regression problems

  115. [126]

    What is the primary difference between supervised and unsupervised learning?

  116. [127]

    Unsupervised learning uses more labels

  117. [128]

    Supervised learning uses no labels

  118. [129]

    Unsupervised learning does not use output labels Y

  119. [130]

    Supervised learning requires big datasets; unsupervised learning works with small datasets

  120. [131]

    Which of the following is NOT a type of unsupervised learning?

  121. [132]

    Dimensionality reduction

  122. [133]

    Predicting stock prices

  123. [134]

    What does clustering aim to achieve?

  124. [135]

    Assign labels to each data point

  125. [136]

    Group data into different clusters based on similarities

  126. [137]

    Classify data based on their labels

  127. [138]

    Predicting future trends

  128. [139]

    Which of the following best describes the role of dimensionality reduction in unsupervised learning?

  129. [140]

    Compressing a large dataset while preserving information

  130. [141]

    Supervising the learning process

  131. [142]

    Clustering data into more groups

  132. [143]

    It adds noise to the dataset

  133. [144]

    What might an unsupervised learning algorithm do with a dataset of patients’ tumor sizes and ages without labels?

  134. [145]

    Diagnose tumors as benign or malignant

  135. [146]

    Assign tumors to two different clusters

  136. [147]

    Supervise the algorithm to give correct answers

  137. [148]

    D.2 Psychology Theory Video

    I don’t know. D.2 Psychology Theory Video

  138. [149]

    What is lateralization?

  139. [150]

    The connection between the two halves of the brain

  140. [151]

    The difference between the two halves of the brain

  141. [152]

    ClassComet 37

    The similarity between the two halves of the brain. ClassComet 37

  142. [153]

    Which hemisphere of the brain is typically associated with language functions for most people?

  143. [154]

    Both hemispheres equally

  144. [155]

    What is the role of the corpus callosum in brain function?

  145. [156]

    It connects two brain hemispheres

  146. [157]

    It controls motor movements of the body

  147. [158]

    It stores long-term memories

  148. [159]

    It processes visual information from both eyes

  149. [160]

    Why is the concept of being strictly ’right-brained’ or ’left-brained’ considered misleading?

  150. [161]

    Each hemisphere of the brain can perform a wide range of functions

  151. [162]

    Only the right hemisphere is active in creative tasks

  152. [163]

    Hemispheres of the brain are involved in most cognitive tasks to varying degrees

  153. [164]

    Only the left hemisphere is active during logical reasoning

  154. [165]

    Which hemisphere of the brain is typically associated with art awareness for most people?

  155. [166]

    Both hemisphere equally

  156. [167]

    What might be a consequence of this procedure?

    Suppose a person has had cut their corpus callosum. What might be a consequence of this procedure?

  157. [168]

    Enhanced coordination between the hemispheres

  158. [169]

    Division of the person’s consciousness in terms of specific brain functions

  159. [170]

    Improved language comprehension

  160. [171]

    Reduced motor control on the left side of the body

  161. [172]

    Why can we name apple on the right side more quickly?

  162. [173]

    Left side of the brain mainly processes language

  163. [174]

    Right side of the brain mainly processes language

  164. [175]

    People are more interested in the objects on the right side

  165. [176]

    How do the two hemispheres of the brain communicate with each other?

  166. [177]

    Through electrical impulses in the brain stem

  167. [178]

    By processing information independently of each other

  168. [179]

    Via the corpus callosum, which transfers information between the hemispheres

  169. [180]

    They do not communicate directly

  170. [182]

    Left part of the brain is better at perceiving logic

  171. [183]

    Functions of two sides of the brain are completely different

  172. [184]

    People who are left-handed use language functions on both sides of the brain

  173. [185]

    What is the relationship between lateralization and the brain’s ability to process sensory information? 38 Zipeng et al

  174. [186]

    Lateralization ensures that both hemispheres process the same sensory data simultaneously

  175. [187]

    Lateralization allows each hemisphere to specialize in processing sensory data from the opposite side of the body

  176. [188]

    Lateralization prevents the brain from perceiving sensory information accurately

  177. [189]

    D.3 Music Theory Video

    Lateralization causes delays in sensory perception. D.3 Music Theory Video

  178. [190]

    What is the term for vertical distance between two notes?

  179. [191]

    How many distinct pitch classes are there within an octave as illustrated by a guitar?

  180. [192]

    On a piano, what do the black keys represent?

  181. [193]

    What is the smallest interval used in music theory?

  182. [194]

    What pattern of tones and semitones defines a major scale?

  183. [195]

    Tone, Tone, Semitone, Tone, Tone, Semitone, Tone

  184. [196]

    Tone, Semitone, Tone, Tone, Semitone, Tone, Tone

  185. [197]

    Tone, Tone, Semitone, Tone, Tone, Tone, Semitone

  186. [198]

    Semitone, Tone, Tone, Semitone, Tone, Tone, Tone

  187. [199]

    If you play a C major scale on the piano, which keys will you press?

  188. [200]

    All white keys from C to the next C

  189. [201]

    A combination of black and white keys starting on C

  190. [202]

    Only the black keys surrounding the C keys

  191. [203]

    When is the Common Practice period?

  192. [204]

    1600-1800 ClassComet 39

  193. [205]

    What is C major scale?

  194. [206]

    C, D, E, F, G, A, B, C

  195. [207]

    A, B, C, D, E, F, G, A, B, C

  196. [208]

    In music theory, what does the term ’interval’ refer to?

  197. [209]

    The distance between two notes

  198. [210]

    The volume of a note

  199. [211]

    The length of a note

  200. [212]

    The speed of a piece

  201. [213]

    What’s the difference between octave and scale?

  202. [214]

    An octave is a series of eight notes, while a scale is a sequence of notes in a specific order

  203. [215]

    An octave is a type of scale, while a scale is a type of chord

  204. [216]

    An octave is a sequence of notes in a specific order, while a scale is a series of eight notes

  205. [217]

    D.4 Latin Video

    An octave is a type of chord, while a scale is a type of scale. D.4 Latin Video

  206. [218]

    What’s the pronunciation of the Latin letter ’C’?

  207. [219]

    Similar to the English "k"

  208. [220]

    ch" like in

    Pronounced as "ch" like in "church"

  209. [221]

    sh" like in

    Pronounced as "sh" like in "church"

  210. [222]

    When does the Latin letter ’I’ act as a consonant?

  211. [223]

    At the end of a word

  212. [224]

    When followed by a consonant

  213. [225]

    When it precedes or is between vowels

  214. [226]

    When it stands alone

  215. [227]

    Which letter in Latin is described as always being followed by ’U’?

  216. [228]

    What is the description the pronunciation of the letter ’S’ in Latin?

  217. [229]

    s" as in

    Always like "s" as in "soot"

  218. [230]

    Silent in most cases

  219. [231]

    Sometimes soft and sometimes hard

  220. [232]

    z" as in

    Always like "z" as in "rose"

  221. [233]

    Which Latin letter is said to have a dual function as both a consonant and a vowel?

  222. [234]

    What is the relevance of the letter ’J’ in English and ’I’ in classical Latin?

  223. [235]

    ’J’ is pronounced differently in Latin than it is in English

  224. [236]

    ’J’ in English originates from ’I’ in Latin

  225. [237]

    ’J’ in English originates from ’J’ in Latin

  226. [238]

    Which of the following is incorrect?

  227. [239]

    The capital Úín Latin looks like our English capital ´W´

  228. [240]

    Ćálways like ´Kín classcal Latin

  229. [241]

    Classical Latin has fewer letters compared to English

  230. [242]

    What’s the correct description of the pronunciation of the letter ’T’ in Latin?

  231. [243]

    Sounds like ’t’ in English

  232. [244]

    Sounds like ’sh’ in English

  233. [245]

    Sounds like ’ch’ in English

  234. [246]

    Sounds like ’th’ in English

  235. [247]

    What is the pronunciation rule for the letter ’Q’ in Latin?

  236. [248]

    It is pronounced like ’k’

  237. [249]

    It is always followed by ’U’, which is not a vowel

  238. [250]

    In Latin, how are Greek aspirated consonants like CH, PH, and TH pronounced?

  239. [251]

    As in ’chapter’, ’phone’, and ’the’

  240. [252]

    With a breath of air after them

  241. [253]

    When my teacher first explains new material, I feel bored

    I don’t know. E Engagment Questionnaire E.1 Behavorial Engagement • When I watch the video, I listen very carefully. (EvsD scale, behavioral section, Item 5) • I want to find out more about the subject matter of the video. (Situational Interest scale, Exploration Intention sec...

  242. [254]

    The danmaku is closely related to the video content

  243. [255]

    The danmaku effectively captures the main points of the video

  244. [256]

    The emotional tone of the danmaku aligns well with the emotions conveyed in the video. 4 . 00 ± 0 . 89 4 . 00 ± 0 . 78 3 . 65 ± 0 . 91 2 . 85 ± 1 . 39 2 . 95 ± 1 . 36 3 . 60 ± 1 . 31 Consistency

  245. [257]

    The danmaku does not contain untrue information(fake data, wrong concepts, etc.) against the video content

  246. [258]

    The danmaku does not include any mislead- ing emotion about the video. 4 . 75 ± 0 . 48 5 . 00 ± 0 . 00 4 . 60 ± 0 . 66 5 . 00 ± 0 . 00 Fluency

  247. [259]

    The danmaku is grammatically correct

  248. [260]

    The danmaku is simple and easy to understand. 4 . 65 ± 0 . 48 4 . 58 ± 0 . 50 4 . 85 ± 0 . 36 4 . 60 ± 0 . 49 Coherence

  249. [261]

    The danmaku interaction is similar to natural, realistic conversations and not overly formal. 4 . 10 ± 0 . 37 4 . 45 ± 0 . 74

  250. [2023]

    arXiv preprint arXiv:2310.19773 (2023)

    Mm-vid: Advancing video understanding with gpt-4v (ision). arXiv preprint arXiv:2310.19773 (2023)

    2023 arXiv

  251. [2024]

    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Moviechat: From dense token to sparse memory for long video understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 18221–18232

Pith tools

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