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Enhancing Deliberativeness: Evaluating the Impact of Multimodal Reflection Nudges

T0 review · 3 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read Presenting reflection prompts as videos improves deliberative quality more than text, image, or audio, even when users say they prefer text.

desk verdict The 'video works best' result is real in the data but can't be cleanly attributed to modality: the image condition drops the text and the video condition adds narration plus visuals. read the letter →

arxiv 2502.03862 v2 pith:7VK4ZIWH submitted 2025-02-06 cs.HC

ShunYi Yeo , Zhuoqun Jiang , Anthony Tang , Simon Tangi Perrault This is my paper · ORCID
classification cs.HC
keywords deliberationdeliberativenessreflectivenudgesmultimodalityonlineself-reflectionlargelanguagemodelsuserstudy
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

The paper asks whether the sensory format of a reflection prompt—text, image, video, or audio—changes how thoughtfully a person writes before joining an online discussion. It reports that format matters: for persona-style direct nudges, video produced significantly more non-redundant arguments than text, image, or audio, even though text was the most preferred format. For storytelling-style indirect nudges, video again led on argument diversity, opinion expression, and justification level, while text scored worst on opinion expression. The authors conclude that reflective nudge design should match modality to nudge type, and that subjective preference is not a reliable guide to deliberative quality. This matters because online deliberation platforms currently rely on text by default, potentially under-servicing complex reflective tasks.

What carries the argument

The carrying mechanism is the multimodal reflective nudge—a short prompt, generated by a large language model and rendering tools, that asks a user to consider another person's perspective (persona) or a narrative (storytelling) before writing. It is presented inside the Reflect interface in one of four modalities: text, image, video, or audio. The experiment's engine is the 2x4 between-subjects comparison of these nudges against five coded measures of deliberativeness: argument repertoire, argument diversity, opinion expression, justification level, and constructiveness. Content equivalence across modalities is maintained by using the same generated script as the source for every modality.

What would settle it

Run the storytelling comparison again with a matched audio script: the same narration presented as audio-only, as narration with a static image, and as narration with moving visuals. If the three produce equal deliberativeness scores, the paper's claim that video modality itself drives the effect is wrong.

Watch

Extended reading notes

Core claim

The central claim is that modality changes deliberativeness, and video is the most consistently effective modality. In a between-subjects experiment with 200 participants, the persona-type nudge delivered as video yielded an average of 3.60 non-redundant arguments, significantly more than text (2.32), image (2.56), or audio (2.60). For the storytelling nudge, video significantly outperformed image on argument diversity (5.52 vs 3.72 themes) and justification level (2.32 vs 1.44), and text was significantly worse than image, video, and audio for opinion expression (0.48 vs 0.76, 0.84, 0.84). The paper interprets these effects through dual-system thinking: text favours fast autonomous reading suited to short direct prompts, while video's combined visual and auditory channels sustain engagement for longer narrative prompts. The conclusion is that the optimal modality depends on the type of reflective nudge, and platforms should offer video rather than defaulting to text.

Load-bearing premise

The central assumption is that the four modality versions contain strictly the same content, so any quality difference must come from the presentation format; but the image version omits the text and the video version adds narration and motion, so the content is not actually identical.

Editorial extensions

If this is right

  • Online deliberation platforms can raise individual opinion quality by offering video-based reflection nudges, especially when the nudge is a narrative.
  • Text-based nudges remain defensible for short persona prompts, but platforms should avoid long text-only storytelling prompts because text scored lowest on opinion expression.
  • Subjective preference data is not sufficient for choosing nudge formats; objective deliberation metrics should drive design decisions.
  • LLM-backed generation makes it feasible to render the same reflective script across modalities, letting platforms adapt to different tasks and users without manual content authoring.

Reading between the lines

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

  • The paper's content-equivalence assumption is incomplete: image prompts were required to contain no wording and video added voice narration and motion, so the video condition carries more information than the image condition; part of video's measured advantage may therefore be an information-richness effect rather than a pure modality effect.
  • A direct replication could isolate the mechanism by using the identical narration in audio-only and video conditions; if the visual channel adds nothing beyond the voice, the video advantage should shrink.
  • The results suggest a testable personalization rule: users with low prior topic knowledge or non-native language backgrounds may benefit more from image or video nudges, since those modalities convey context without requiring dense reading.
  • Because storytelling videos ran 90 to 120 seconds while persona videos ran 9 to 12 seconds, the duration difference is confounded with nudge type; shorter narrative videos might not reproduce the storytelling benefit.
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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 / 5 minor

Summary. This paper investigates whether presenting reflective nudges in different modalities (text, image, video, audio) affects the quality of online deliberation. It reports two user studies: Study 1 (N=20) measures subjective modality preference for two nudge types, persona (direct) and storytelling (indirect), finding text most preferred for persona and video for storytelling. Study 2 (N=200, between-subjects, 25 per condition) measures five deliberativeness dimensions under each modality and nudge type, reporting that video yields higher argument repertoire for persona and higher argument diversity, opinion expression, and justification level for storytelling. The paper concludes that video is generally the most effective modality and advocates matching modality to nudge type. The central causal claim rests on the assertion in Section 3.3 that content is 'strictly the same' across modalities, so that observed differences are attributable to modality alone.

Significance. If the causal claim held, the paper would make a useful empirical contribution to online deliberation design, extending prior text-only nudging work to multimodal presentation and offering practical guidance. The study has genuine strengths: a pre-specified power analysis, inter-coder reliability above 0.83 for all dependent variables, use of established deliberativeness measures, and a reasonably powered between-subject design in Study 2. However, the central attribution of effects to modality is undermined by a content-equivalence confound that is both internally contradicted and unvalidated. The paper's own Limitations section does not mention this confound. With the finding reframed as an effect of multimodal presentation formats (which differ in information content), the study would still be of interest, but the causal language in the abstract and conclusion is not currently supported.

major comments (3)
  1. [Section 3.3, Appendix Table 4] The central premise that 'the content across all modalities is strictly the same, just presented differently by their own modality' is contradicted by the implementation. The image prompt explicitly requires 'The image has no wordings,' so the persona perspective or story text is absent from image stimuli, while the video prompt requires the voice narration to 'follow the script exactly as provided' and adds 'a background relevant to the persona's occupation' and photo-realistic moving visuals. Thus the video condition presents the full verbal script plus additional scene information, the audio condition presents only the narration, the text condition only the script, and the image condition only an illustration. These differences in information content, not modality alone, could explain the observed video advantages in argument repertoire (Section 6.1.1) and argument diversity/opinion expression/justification (Sections 6.1.2-6.1.4). This is a load-bearing confound because the paper's stated design goal was to hold content fixed in order to attribute effects to modality.
  2. [Section 3.3 / Section 8] No manipulation check or content-equivalence validation is reported. Given the documented differences in stimulus composition, the authors need to provide evidence that participants perceived the same underlying content across modalities, or substantially temper the causal interpretation. The Limitations section (Section 8) lists several limitations but does not acknowledge this confound, which is directly relevant to the paper's main claim.
  3. [Section 6.1.6, Table 2, Introduction] The summary of Study 2 overstates the persona result. For the direct (persona) nudge, only Argument Repertoire reached significance (Table 2, p<.01); Argument Diversity, Opinion Expression, Justification Level, and Constructiveness were all non-significant. The statement 'Video generally emerges as the most effective modality for the direct reflective nudge' is therefore too strong, and the Introduction's claim that 'video leads to higher level of deliberative quality for persona' should be corrected to specify the single significant dimension.
minor comments (5)
  1. [Section 6.1] The text says post-hoc comparisons were applied to 'the eight measures of deliberativeness,' but Section 5.2 defines only five dependent variables; this should be corrected to 'five measures' or otherwise clarified.
  2. [Figure 4 caption] The caption contains a typo: 'ANCOV A' should be 'ANCOVA'.
  3. [Table 6] The education category 'Post-Graudate Degree' is misspelled; it should read 'Post-Graduate Degree.'
  4. [Section 4.5 / Section 5.5] Study 1 required participants to write at least 30 words, but Study 2 does not state a minimum word count; please clarify whether the same instruction applied in Study 2, since length constraints could interact with deliberativeness measures.
  5. [Section 4.7] The ANCOVA in Study 1 is conducted on only 20 participants with four covariates; the authors should acknowledge in Section 8 that this small sample size limits the reliability of the preference ranking analysis, which is currently described without such a caveat.

Circularity Check

0 steps flagged · score 0.0 of 10

Empirical between-subjects user study with no fitted parameters or derived equations; the reported modality effects are observed outcomes, not consequences of the paper's definitions or self-citations.

full rationale

The paper contains no formal derivation, no predictive model, and no fitted parameter that is later renamed as a finding. Studies 1 and 2 are randomized experiments (20 and 200 participants) analyzed with ANCOVA and pairwise comparisons. The dependent variables—argument repertoire, argument diversity, opinion expression, justification level, and constructiveness—are coded from participants' free-text responses by two coders with reported Cohen's Kappa values, and the coding schemes are defined independently of the modality manipulation; they are not defined in terms of the experimental conditions. The dual-system framework in Section 2.4 is used after the fact to interpret qualitative feedback, not to compute the quantitative outcomes, so no prediction reduces to its input by construction. The only potentially vulnerable step is the Section 3.3 assertion that content is 'strictly the same' across modalities; the implementation details (images must contain no wordings; video adds voice narration and visual backgrounds) mean the conditions differ in information content, which is a threat to internal validity and to causal attribution, but it is not a circularity because the observed differences are empirically measured rather than logically entailed by the stimulus construction. The self-citations to Yeo et al. [197] are used to justify the selection of persona/storytelling nudge types and the deliberativeness coding scheme; this is standard methodological borrowing from prior empirical work, the cited work contains its own independent evaluation, and the current paper's central contribution—comparing modalities within each nudge type—does not reduce to that citation. No load-bearing self-citation chain, imported uniqueness theorem, ansatz smuggled via citation, or renaming of a known result was found. The paper should therefore receive the low end of the circularity scale.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper's empirical claims rest on measurement assumptions rather than mathematical axioms. The key domain assumptions are the operationalization of deliberativeness through five content-analysis measures, the equivalence of AI-generated stimuli across modalities, and the use of dual-system theory as an interpretive lens.

assumptions (3)
  • domain assumption The five content-analysis measures (argument repertoire, argument diversity, rationality, constructiveness) validly operationalize deliberativeness.
    The paper adopts these from prior work (e.g., [197]) and uses them as dependent variables without further validation in this study (Section 5.2).
  • domain assumption The AI-generated stimuli are equivalent across modalities in content and quality.
    The paper assumes this to isolate modality effects, but image and video differ in information content (Section 3.3).
  • domain assumption Dual-system theory provides a valid interpretive framework for modality effects.
    Used to explain qualitative feedback post hoc; not tested as a prediction (Section 2.4).

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Cite this review

Pith. "Pith review of Enhancing Deliberativeness: Evaluating the Impact of Multimodal Reflection Nudges." pith.science (2026). https://pith.science/paper/7VK4ZIWH

@misc{pith2026250203862,
  author       = {Pith},
  title        = {Pith review of: Enhancing Deliberativeness: Evaluating the Impact of Multimodal Reflection Nudges},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7VK4ZIWH}},
  note         = {Machine review of arXiv:2502.03862}
}
read the original abstract

Nudging participants with text-based reflective nudges enhances deliberation quality on online deliberation platforms. The effectiveness of multimodal reflective nudges, however, remains largely unexplored. Given the multi-sensory nature of human perception, incorporating diverse modalities into self-reflection mechanisms has the potential to better support various reflective styles. This paper explores how presenting reflective nudges of different types (direct: persona and indirect: storytelling) in different modalities (text, image, video and audio) affects deliberation quality. We conducted two user studies with 20 and 200 participants respectively. The first study identifies the preferred modality for each type of reflective nudges, revealing that text is most preferred for persona and video is most preferred for storytelling. The second study assesses the impact of these modalities on deliberation quality. Our findings reveal distinct effects associated with each modality, providing valuable insights for developing more inclusive and effective online deliberation platforms.

Figures

Figures reproduced from arXiv: 2502.03862 by the authors.

Figure 1
Figure 1. 1: In online deliberation, the quality of collective discussions is directly influenced by the depth of individual reflection [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Example of the Two Reflective Nudges and their Respective Four Modalities. [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Key features in interface. 1: Users can click on the [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Mean ranking for the four modalities for the direct reflective nudge (left) and indirect reflective nudge (right) with 1 [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]
Figure 5
Figure 5. Figure 5: Count of participants who shared the same feedback on the depth of self-reflection and speed of delivery for the four [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]
Figure 6
Figure 6. Figure 6: Butterfly Chart for the Text Modality across Both Nudge Types [PITH_FULL_IMAGE:figures/full_fig_p027_6.png]
Figure 7
Figure 7. Figure 7: Butterfly Chart for the Image Modality across Both Nudge Types [PITH_FULL_IMAGE:figures/full_fig_p027_7.png]
Figure 8
Figure 8. Figure 8: Butterfly Chart for the Video Modality across Both Nudge Types [PITH_FULL_IMAGE:figures/full_fig_p027_8.png]
Figure 9
Figure 9. Figure 9: Butterfly Chart for the Audio Modality across Both Nudge Types [PITH_FULL_IMAGE:figures/full_fig_p027_9.png]

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Reference graph

Works this paper leans on

206 extracted references · 72 canonical work pages

  1. [1]

    [n. d.]. Consider.it is the only forum to visually summarize what your community thinks and why — consider.it. https://consider.it/tour. [Accessed 12-09-2024]

    2024

  2. [2]

    [n. d.]. VARK ® Research:What do we know about VARK? - — vark-learn.com. https://vark-learn.com/research-statistics/. [Accessed 12-09-2024]

    2024

  3. [3]

    Oxford English Dictionary

    1989. Oxford English Dictionary. Oxford University Press. Deliberation

    1989

  4. [4]

    Ross C Anderson, Meg Guerreiro, and Joanna Smith. 2016. Are all biases bad? Collaborative grounded theory in developmental evaluation of education policy. Journal of Multidisciplinary Evaluation 12, 27 (2016), 44–57

    2016

  5. [5]

    Kevin Arceneaux and Ryan J Vander Wielen. 2017. Taming intuition: How reflection minimizes partisan reasoning and promotes democratic accountability . Cambridge University Press

    2017

  6. [6]

    Jonathan Barnes Aristotle et al . 1984. The complete works of Aristotle . Vol. 2. Princeton University Press Princeton, NJ

    1984

  7. [7]

    Robert K Atkinson, Richard E Mayer, and Mary Margaret Merrill. 2005. Fostering social agency in multimedia learning: Examining the impact of an animated agent’s voice. Contemporary Educational Psychology 30, 1 (2005), 117–139

    2005

  8. [8]

    Paul Ayres and John Sweller. 2005. The split-attention principle in multimedia learning. The Cambridge handbook of multimedia learning 2 (2005), 135–146

    2005

Show all 206 references
  1. [9]

    Robert F Bales. 1983. Overview of the SYMLOG system. Woodland Hills, CA (1983)

    1983

  2. [10]

    Bonnie N Bartholomeus and DG Doehring. 1972. Acquisition of visual-auditory associations by good and excellent readers. Perceptual and Motor Skills 35, 3 (1972), 847–855

    1972

  3. [11]

    C Daniel Batson, Shannon Early, and Giovanni Salvarani. 1997. Perspective taking: Imagining how another feels versus imaging how you would feel. Per- sonality and social psychology bulletin 23, 7 (1997), 751–758

    1997

  4. [12]

    Dieter Beck and Rudolf Fisch. 2000. Argumentation and emotional processes in group decision-making: Illustration of a multilevel interaction process analysis approach. Group Processes & Intergroup Relations 3, 2 (2000), 183–201

    2000

  5. [13]

    Dawn Birch and Michael Gardiner. 2005. Students’ perceptions of technology- based marketing courses. In Proceedings of the 2005 Australian and New Zealand Marketing Academy Conference (ANZMAC 2005)

    2005

  6. [14]

    Herbert G Birch and Lillian Belmont. 1964. Auditory-visual integration in normal and retarded readers. American Journal of Orthopsychiatry 34, 5 (1964), 852

    1964

  7. [15]

    Laura W Black, Howard T Welser, Dan Cosley, and Jocelyn M DeGroot. 2011. Self-governance through group discussion in Wikipedia: Measuring deliberation in online groups. Small Group Research 42, 5 (2011), 595–634

    2011

  8. [16]

    Daniel Bodemer and Rolf Ploetzner. 2002. Encouraging the active integration of information during learning with multiple and interactive representations. In international workshop on dynamic visualizations and learning, Tubingen, Germany

    2002

  9. [17]

    James Bohman. 2000. Public deliberation: Pluralism, complexity, and democracy . MIT press

    2000

  10. [18]

    Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016. Man is to computer programmer as woman is to homemaker? debiasing word embeddings. Advances in neural information processing systems 29 (2016)

    2016

  11. [19]

    Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. 2021. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021)

    2021 arXiv

  12. [20]

    Keri Borzello. 2018. The benefits of a multimodality approach to teaching and learning. In. In New Perspectives in Science Education. Conference Proceedings . 141–143

    2018

  13. [21]

    Pauline Bowen, Joan Hash, and Mark Wilson. 2006. Information security hand- book: a guide for managers . Technical Report. National Institute of Standards and Technology

    2006

  14. [22]

    Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877–1901

    2020

  15. [23]

    Jerome Bruner. 1991. The narrative construction of reality. Critical inquiry 18, 1 (1991), 1–21

    1991

  16. [24]

    Kaya Cahit. 2015. Internal validity: A must in research designs. Educational Research and Reviews 10, 2 (2015), 111–118

    2015

  17. [25]

    Kelly Caine. 2016. Local standards for sample size at CHI. In Proceedings of the 2016 CHI conference on human factors in computing systems . 981–992

    2016

  18. [26]

    Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan. 2017. Semantics derived automatically from language corpora contain human-like biases. Science 356, 6334 (2017), 183–186

    2017

  19. [27]

    Donald T Campbell and Julian C Stanley. 2015. Experimental and quasi- experimental designs for research . Ravenio books

    2015

  20. [28]

    Joseph N Cappella, Vincent Price, and Lilach Nir. 2002. Argument repertoire as a reliable and valid measure of opinion quality: Electronic dialogue during campaign 2000. Political Communication 19, 1 (2002), 73–93

    2002

  21. [29]

    Ana Caraban, Evangelos Karapanos, Daniel Gonçalves, and Pedro Campos

  22. [30]

    Robert Cavalier, Miso Kim, and Zachary Sam Zaiss. 2009. Deliberative democ- racy, online discussion, and project PICOLA (Public informed citizen online assembly). Online deliberation: Design, research, and practice/Eds. Davies T., Gangadharan SP–Stanford, CA: Center for the st...

    2009

  23. [31]

    Simone Chambers. 2003. Deliberative democratic theory. Annual review of political science 6, 1 (2003), 307–326

    2003

  24. [32]

    Paul Chandler and John Sweller. 1992. The split-attention effect as a factor in the design of instruction. British Journal of Educational Psychology 62, 2 (1992), 233–246

    1992

  25. [33]

    Thomas Christiano and John Christman. 2009. Debates in Political Philosophy . Wiley Online Library

    2009

  26. [34]

    Soon Ae Chun and Luis F Luna Reyes. 2012. Social media in government. , 441–445 pages

    2012

  27. [35]

    2023.E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning

    Ruth C Clark and Richard E Mayer. 2023.E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning . john Wiley & sons

    2023

  28. [36]

    Fofi Constantinidou and Susan Baker. 2002. Stimulus modality and verbal learning performance in normal aging. Brain and language 82, 3 (2002), 296– 311

    2002

  29. [37]

    Thomas D Cook, Donald Thomas Campbell, and Arles Day. 1979. Quasi- experimentation: Design & analysis issues for field settings . Vol. 351. Houghton Mifflin Boston

    1979

  30. [38]

    Alan Cooper. 1999. The inmates are running the asylum. Indianapolis, IA: SAMS. Macmillan (1999)

    1999

  31. [39]

    Alan Cooper, Robert Reimann, and David Cronin. 2007. About face 3: the essentials of interaction design . John Wiley & Sons

    2007

  32. [40]

    Kelley Cotter. 2019. Playing the visibility game: How digital influencers and algorithms negotiate influence on Instagram. New media & society 21, 4 (2019), 895–913

    2019

  33. [41]

    Lincoln Dahlberg. 2001. Computer-mediated communication and the public sphere: A critical analysis. Journal of Computer-mediated communication 7, 1 (2001), JCMC714

    2001

  34. [42]

    Edgar Dale. 1969. Audiovisual methods in teaching. (1969)

    1969

  35. [43]

    Hai Dang, Karim Benharrak, Florian Lehmann, and Daniel Buschek. 2022. Be- yond text generation: Supporting writers with continuous automatic text sum- maries. In Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology. 1–13

    2022

  36. [44]

    Patricia N Daniel and Robert S Tacker. 1974. Preferred modality of stimulus input and memory for CVC trigrams. The Journal of Educational Research 67, 6 (1974), 255–258

    1974

  37. [45]

    Todd Davies and Reid Chandler. 2013. Online deliberation design: Choices, criteria, and evidence. arXiv preprint arXiv:1302.5177 (2013)

    2013 arXiv

  38. [46]

    Todd Davies and Seeta Peña Gangadharan. 2009. Online deliberation: Design, research, and practice. (2009)

    2009

  39. [47]

    Robert Dawson. 2011. How significant is a boxplot outlier? Journal of Statistics Education 19, 2 (2011)

    2011

  40. [48]

    Katrien De Westelinck, Martin Valcke, Brigitte De Craene, and Paul Kirschner

  41. [49]

    Markus Deimann and John Keller. 2006. Volitional aspects of multimedia learn- ing. Journal of educational multimedia and hypermedia 15, 2 (2006), 137–158

    2006

  42. [50]

    Rianne Dekker and Victor Bekkers. 2015. The contingency of governments’ responsiveness to the virtual public sphere: A systematic literature review and meta-synthesis. Government Information Quarterly 32, 4 (2015), 496–505

    2015

  43. [51]

    Nicholas Diakopoulos and Mor Naaman. 2011. Towards quality discourse in online news comments. In Proceedings of the ACM 2011 conference on Computer supported cooperative work. 133–142

    2011

  44. [52]

    E Dolzhich and S Dmitrichenkova. 2019. Multimodality in education. In INTED2019 Proceedings. IATED, 1980–1984

    2019

  45. [53]

    John S Dryzek. 2002. Deliberative democracy and beyond: Liberals, critics, contes- tations. Oxford University Press, USA. CHI ’25, April 26-May 1, 2025, Yokohama, Japan Yeo et al

    2002

  46. [54]

    John S Dryzek. 2009. Democratization as deliberative capacity building. Com- parative political studies 42, 11 (2009), 1379–1402

    2009

  47. [55]

    Jérôme Duberry. 2022. Artificial intelligence and democracy: risks and promises of AI-mediated citizen–government relations. In Artificial Intelligence and Democracy. Edward Elgar Publishing

    2022

  48. [56]

    Rita Dunn. 1984. Learning style: State of the science. Theory into practice 23, 1 (1984), 10–19

    1984

  49. [57]

    Rita Dunn, Shirley A Griggs, Jeffery Olson, Mark Beasley, and Bernard S Gorman

  50. [58]

    Rita Stafford Dunn and Kenneth J Dunn. 1993. Teaching secondary students through their individual learning styles: Practical approaches for grades 7-12. (No Title) (1993)

    1993

  51. [59]

    Enas El-Saftawy, Ahmed A Abdel Latif, Asmaa M ShamsEldeen, Mansour A Alghamdi, Amal M Mahfoz, and Basma Emad Aboulhoda. 2024. Influence of applying VARK learning styles on enhancing teaching skills: application of learning theories. BMC Medical Education 24, 1 (2024), 1034

    2024

  52. [60]

    Charles Fadel and Cheryl Lemke. 2008. Multimodal learning through media: What the research says. San Jose, CA: Cisco Systems (2008), 1–24

    2008

  53. [61]

    Hans-Joachim Fietkau and Matthias Trénel. 2002. Interaktionsmuster in einem Diskursverfahren zur Indikatorenentwicklung im Umweltbereich. (2002)

    2002

  54. [62]

    Neil Fleming and C Mills. 2001. VARK: A guide to learning styles. Retrieved November 30 (2001), 2004

    2001

  55. [63]

    Neil D Fleming. 1995. I’m different; not dumb. Modes of presentation (VARK) in the tertiary classroom. In Research and development in higher education, Proceedings of the 1995 Annual Conference of the Higher Education and Research Development Society of Australasia (HERDSA), H...

    1995

  56. [64]

    Neil D Fleming and Colleen Mills. 1992. Not another inventory, rather a catalyst for reflection. To improve the academy 11, 1 (1992), 137–155

    1992

  57. [65]

    JD Fletcher and Sigmund Tobias. 2005. The multimedia principle.The Cambridge handbook of multimedia learning 117 (2005), 133

    2005

  58. [66]

    Brian J Fogg. 2002. Persuasive technology: using computers to change what we think and do. Ubiquity 2002, December (2002), 2

    2002

  59. [67]

    Benjamin Franklin, Leonard Woods Labaree, and Whitfield Jenks Bell. 1956. Mr. Franklin: a selection from his personal letters. (No Title) (1956)

    1956

  60. [68]

    Natasha Freidus and Michelle Hlubinka. 2002. Digital storytelling for reflective practice in communities of learners. ACM SIGGROUP Bulletin 23, 2 (2002), 24–26

    2002

  61. [69]

    Adam D Galinsky and Gordon B Moskowitz. 2000. Perspective-taking: decreas- ing stereotype expression, stereotype accessibility, and in-group favoritism. Journal of personality and social psychology 78, 4 (2000), 708

    2000

  62. [70]

    Jie Gao, Kenny Tsu Wei Choo, Junming Cao, Roy Ka-Wei Lee, and Simon Perrault

  63. [71]

    Howard E Gardner. 2011. Frames of mind: The theory of multiple intelligences . Basic books

    2011

  64. [72]

    Noelia Gargallo Camarillas. 2018. Perceptual learning styles and multimodality in EFL education: the digital learning style and social networks. (2018)

    2018

  65. [73]

    Eugene Garver. 2004. For the sake of argument: Practical reasoning, character, and the ethics of belief . University of Chicago Press

    2004

  66. [74]

    John Gastil. 2008. Political communication and deliberation . Sage

    2008

  67. [75]

    John Gastil and Laura Black. 2007. Public deliberation as the organizing principle of political communication research. Journal of Public Deliberation 4, 1 (2007)

    2007

  68. [76]

    Alex Goddard and Alex Gillespie. 2023. Textual Indicators of Deliberative Dialogue: A Systematic Review of Methods for Studying the Quality of Online Dialogues. Social Science Computer Review 41, 6 (2023), 2364–2385

    2023

  69. [77]

    Robert E Goodin. 2000. Democratic deliberation within. Phil. & Pub. Aff. 29 (2000), 81

    2000

  70. [78]

    Robert E Goodin and Simon J Niemeyer. 2003. When does deliberation begin? Internal reflection versus public discussion in deliberative democracy. Political Studies 51, 4 (2003), 627–649

    2003

  71. [79]

    Todd Graham and Tamara Witschge. 2003. In search of online deliberation: Towards a new method for examining the quality of online discussions. (2003)

    2003

  72. [80]

    Anthony M Grant. 2001. Rethinking psychological mindedness: Metacognition, self-reflection, and insight. Behaviour Change 18, 1 (2001), 8–17

    2001

  73. [81]

    Anthony M Grant. 2003. The impact of life coaching on goal attainment, metacognition and mental health. Social Behavior and Personality: an inter- national journal 31, 3 (2003), 253–263

    2003

  74. [82]

    Anthony M Grant, John Franklin, and Peter Langford. 2002. The self-reflection and insight scale: A new measure of private self-consciousness. Social Behavior and Personality: an international journal 30, 8 (2002), 821–835

    2002

  75. [83]

    Melanie C Green and Timothy C Brock. 2000. The role of transportation in the persuasiveness of public narratives. Journal of personality and social psychology 79, 5 (2000), 701

    2000

  76. [84]

    Jürgen Habermas. 2015. Between facts and norms: Contributions to a discourse theory of law and democracy . John Wiley & Sons

    2015

  77. [85]

    Robert Hackett and Yuezhi Zhao. 1998. Sustaining democracy?: Journalism and the politics of objectivity

    1998

  78. [86]

    Jeffrey T Hancock, Mor Naaman, and Karen Levy. 2020. AI-mediated commu- nication: Definition, research agenda, and ethical considerations. Journal of Computer-Mediated Communication 25, 1 (2020), 89–100

    2020

  79. [87]

    Pelle Guldborg Hansen and Andreas Maaløe Jespersen. 2013. Nudge and the manipulation of choice: A framework for the responsible use of the nudge approach to behaviour change in public policy.European journal of risk regulation 4, 1 (2013), 3–28

    2013

  80. [88]

    Marian Harbach, Markus Hettig, Susanne Weber, and Matthew Smith. 2014. Using personal examples to improve risk communication for security & privacy decisions. In Proceedings of the SIGCHI conference on human factors in computing systems. 2647–2656

    2014

  81. [89]

    S Hazari. 2004. Applying instructional design theories to improve efficacy of technology-assisted presentations. Journal of Instruction Delivery Systems 18, 2 (2004), 24–33

    2004

  82. [90]

    Mary Hegarty and Marcel-Adam Just. 1993. Constructing mental models of machines from text and diagrams. Journal of memory and language 32, 6 (1993), 717–742

    1993

  83. [91]

    Markus Holdo. 2020. Meta-deliberation: Everyday acts of critical reflection in deliberative systems. Politics 40, 1 (2020), 106–119

    2020

  84. [92]

    Benjamin D Horne, Sibel Adali, and Sujoy Sikdar. 2017. Identifying the social signals that drive online discussions: A case study of reddit communities. In 2017 26th International Conference on Computer Communication and Networks (ICCCN). IEEE, 1–9

    2017

  85. [93]

    Po-Sen Huang, Huan Zhang, Ray Jiang, Robert Stanforth, Johannes Welbl, Jack Rae, Vishal Maini, Dani Yogatama, and Pushmeet Kohli. 2019. Reducing sen- timent bias in language models via counterfactual evaluation. arXiv preprint arXiv:1911.03064 (2019)

    2019 arXiv

  86. [94]

    2009.Talking together: Public deliberation and political participation in America

    Lawrence R Jacobs, Fay Lomax Cook, and Michael X Delli Carpini. 2009.Talking together: Public deliberation and political participation in America . University of Chicago Press

    2009

  87. [95]

    Maurice Jakesch, Advait Bhat, Daniel Buschek, Lior Zalmanson, and Mor Naa- man. 2023. Co-writing with opinionated language models affects users’ views. In Proceedings of the 2023 CHI conference on human factors in computing systems . 1–15

    2023

  88. [96]

    Davy Janssen and Raphaël Kies. 2004. Online forums and deliberative democracy: Hypotheses, variables and methodologies. InConference on Empirical Approaches to Deliberative Politics

    2004

  89. [97]

    Marijn Janssen and Natalie Helbig. 2018. Innovating and changing the policy- cycle: Policy-makers be prepared! Government Information Quarterly 35, 4 (2018), S99–S105

    2018

  90. [98]

    Rebecca L Johnson, Giada Pistilli, Natalia Menédez-González, Leslye Denisse Dias Duran, Enrico Panai, Julija Kalpokiene, and Donald Jay Bertulfo

  91. [99]

    Daniel Kahneman. 2002. Maps of bounded rationality: A perspective on intuitive judgement and choice. (2002)

    2002

  92. [100]

    Daniel Kahneman. 2011. Thinking, fast and slow. Farrar, Straus and Giroux (2011)

    2011

  93. [101]

    Daniel Kahneman. 2012. Two systems in the mind. Bulletin of the American Academy of Arts and Sciences 65, 2 (2012), 55–59

    2012

  94. [102]

    Daniel Kahneman, Jack L Knetsch, and Richard H Thaler. 1991. Anomalies: The endowment effect, loss aversion, and status quo bias. Journal of Economic perspectives 5, 1 (1991), 193–206

    1991

  95. [103]

    Stamatis Karnouskos. 2020. Artificial intelligence in digital media: The era of deepfakes. IEEE Transactions on Technology and Society 1, 3 (2020), 138–147

    2020

  96. [104]

    G Kearsley. 2000. Online education: Learning and teaching in cyberspace. Wadsworth Thomson Learning (2000)

    2000

  97. [105]

    Oscar K Keyes and Adam Hyland. 2023. Hands are hard: unlearning how we talk about machine learning in the arts. Tradition Innovations in Arts, Design, and Media Higher Education 1, 1 (2023), 4

    2023

  98. [106]

    Hyunwoo Kim, Haesoo Kim, Kyung Je Jo, and Juho Kim. 2021. StarryThoughts: facilitating diverse opinion exploration on social issues. Proceedings of the ACM on Human-Computer Interaction 5, CSCW1 (2021), 1–29

    2021

  99. [107]

    Hyunwoo Kim, Eun-Young Ko, Donghoon Han, Sung-Chul Lee, Simon T Per- rault, Jihee Kim, and Juho Kim. 2019. Crowdsourcing perspectives on public policy from stakeholders. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems . 1–6

    2019

  100. [108]

    Silvia Knobloch-Westerwick. 2014. Choice and preference in media use: Advances in selective exposure theory and research . Routledge

    2014

  101. [109]

    David A Kolb. 2014. Experiential learning: Experience as the source of learning and development. FT press

    2014

  102. [110]

    Svetlana Koltovskaia. 2020. Student engagement with automated written correc- tive feedback (AWCF) provided by Grammarly: A multiple case study.Assessing Writing 44 (2020), 100450

    2020

  103. [111]

    Travis Kriplean, Jonathan Morgan, Deen Freelon, Alan Borning, and Lance Ben- nett. 2012. Supporting reflective public thought with considerit. In Proceedings Multimodal Reflection Nudges for Deliberativeness CHI ’25, April 26-May 1, 2025, Yokohama, Japan of the ACM 2012 confer...

    2012

  104. [112]

    Travis Kriplean, Jonathan T Morgan, Deen Freelon, Alan Borning, and Lance Bennett. 2011. ConsiderIt: Improving structured public deliberation. In CHI’11 Extended Abstracts on Human Factors in Computing Systems . 1831–1836

    2011

  105. [113]

    Travis Kriplean, Michael Toomim, Jonathan Morgan, Alan Borning, and Amy J Ko. 2012. Is this what you meant? Promoting listening on the web with reflect. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems . 1559–1568

    2012

  106. [114]

    Will Kymlicka. 2002. Contemporary political philosophy: an introduction. New York (2002)

    2002

  107. [115]

    Lucia Labajová. 2023. The state of AI: Exploring the perceptions, credibility, and trustworthiness of the users towards AI-Generated Content

    2023

  108. [116]

    Markus Langer, Cornelius J König, Caroline Back, and Victoria Hemsing. 2023. Trust in Artificial Intelligence: Comparing trust processes between human and automated trustees in light of unfair bias. Journal of Business and Psychology 38, 3 (2023), 493–508

    2023

  109. [117]

    M Shaune Lawton and Robert D Seim. 1973. Developmental investigation of tactual-visual integration and reading achievement. Perceptual and Motor Skills 36, 2 (1973), 375–382

    1973

  110. [118]

    Khanal Laxman, Shah Sandip, and Koirala Sarun. 2014. Exploration of preferred learning styles in medical education using VARK modal. Russian Open Medical Journal 3, 3 (2014), 305

    2014

  111. [119]

    Melissa Leach, Andrew Charles Stirling, and Ian Scoones. 2010. Dynamic sus- tainabilities: technology, environment, social justice . Taylor & Francis

    2010

  112. [120]

    Adrienne Y Lee and Alexia N Bowers. 1997. The effect of multimedia components on learning. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Vol. 41. SAGE Publications Sage CA: Los Angeles, CA, 340–344

    1997

  113. [121]

    Min Kyung Lee, Sara Kiesler, and Jodi Forlizzi. 2011. Mining behavioral eco- nomics to design persuasive technology for healthy choices. In Proceedings of the sigchi conference on human factors in computing systems . 325–334

    2011

  114. [122]

    Thomas C Leonard. 2008. Richard H. Thaler, Cass R. Sunstein, Nudge: Improving decisions about health, wealth, and happiness: Yale University Press, New Haven, CT, 2008, 293 pp, 26.00

    2008

  115. [123]

    Kristen Lovejoy and Gregory D Saxton. 2012. Information, community, and action: How nonprofit organizations use social media. Journal of computer- mediated communication 17, 3 (2012), 337–353

    2012

  116. [124]

    Maryann Kiely Lovelace. 2005. Meta-analysis of experimental research based on the Dunn and Dunn model. The Journal of Educational Research 98, 3 (2005), 176–183

    2005

  117. [125]

    Li Lucy and David Bamman. 2021. Gender and representation bias in GPT-3 gen- erated stories. In Proceedings of the Third Workshop on Narrative Understanding . 48–55

    2021

  118. [126]

    RE Mayer. 2005. The Cambridge handbook of multimedia learning . Cambridge University Press

    2005

  119. [127]

    RE Mayer. 2005. Cognitive theory of multimedia learning

    2005

  120. [128]

    Richard E Mayer. 1989. Systematic thinking fostered by illustrations in scientific text. Journal of educational psychology 81, 2 (1989), 240

    1989

  121. [129]

    Richard E Mayer. 2002. Multimedia learning. In Psychology of learning and motivation. Vol. 41. Elsevier, 85–139

    2002

  122. [130]

    Richard E Mayer and Richard B Anderson. 1991. Animations need narrations: An experimental test of a dual-coding hypothesis. Journal of educational psychology 83, 4 (1991), 484

    1991

  123. [131]

    Richard E Mayer and Richard B Anderson. 1992. The instructive animation: Helping students build connections between words and pictures in multimedia learning. Journal of educational Psychology 84, 4 (1992), 444

    1992

  124. [132]

    Richard E Mayer and Joan K Gallini. 1990. When is an illustration worth ten thousand words? Journal of educational psychology 82, 4 (1990), 715

    1990

  125. [133]

    Richard E Mayer, Kathryn Steinhoff, Gregory Bower, and Rebecca Mars. 1995. A generative theory of textbook design: Using annotated illustrations to fos- ter meaningful learning of science text. Educational technology research and development 43 (1995), 31–41

    1995

  126. [134]

    Mary L McHugh. 2012. Interrater reliability: the kappa statistic. Biochemia medica 22, 3 (2012), 276–282

    2012

  127. [135]

    Alexey N Medvedev, Renaud Lambiotte, and Jean-Charles Delvenne. 2019. The anatomy of Reddit: An overview of academic research. Dynamics on and of Complex Networks III: Machine Learning and Statistical Physics Approaches 10 (2019), 183–204

    2019

  128. [136]

    Hila Mehr. 2017. Artificial Intelligence for Citizen Services and Government. Havard Kenedy School (2017)

    2017

  129. [137]

    Sanju Menon, Weiyu Zhang, and Simon T Perrault. 2020. Nudge for delibera- tiveness: How interface features influence online discourse. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems . 1–13

    2020

  130. [138]

    Lori S Mestre. 2010. Matching up learning styles with learning objects: What’s effective? Journal of Library Administration 50, 7-8 (2010), 808–829

    2010

  131. [139]

    Pamela Miller. 2001. Learning Styles: The Multimedia of the Mind. Research Report. (2001)

    2001

  132. [140]

    Gianluca Misuraca and Colin Van Noordt. 2020. AI Watch-Artificial Intelligence in public services: Overview of the use and impact of AI in public services in the EU. JRC Research Reports JRC120399 (2020)

    2020

  133. [141]

    Jennifer A Moon. 2013. A handbook of reflective and experiential learning: Theory and practice. Routledge

    2013

  134. [142]

    Roxana Moreno and Richard E Mayer. 1999. Cognitive principles of multimedia learning: The role of modality and contiguity. Journal of educational psychology 91, 2 (1999), 358

    1999

  135. [143]

    Seyed Yaghoub Mousavi, Renae Low, and John Sweller. 1995. Reducing cognitive load by mixing auditory and visual presentation modes. Journal of educational psychology 87, 2 (1995), 319

    1995

  136. [144]

    Lala Muradova. 2021. Seeing the other side? Perspective-taking and reflective political judgements in interpersonal deliberation. Political Studies 69, 3 (2021), 644–664

    2021

  137. [145]

    Tom Murray, Lynn Stephens, Beverly Park Woolf, Leah Wing, Xiaoxi Xu, and Natasha Shrikant. 2013. Supporting social deliberative skills online: the effects of reflective scaffolding tools. In Online Communities and Social Computing: 5th International conference, OCSC 2013, Held...

    2013

  138. [146]

    Shinichi Nakagawa. 2004. A farewell to Bonferroni: the problems of low statis- tical power and publication bias. Behavioral ecology 15, 6 (2004), 1044–1045

    2004

  139. [147]

    Mitchell J Nathan, Walter Kintsch, and Emilie Young. 1992. A theory of algebra- word-problem comprehension and its implications for the design of learning environments. Cognition and instruction 9, 4 (1992), 329–389

    1992

  140. [148]

    DI Newble and Margaret I Gordon. 1985. The learning style of medical students. Medical Education 19, 1 (1985), 3–8

    1985

  141. [149]

    Vlad Niculae and Cristian Danescu-Niculescu-Mizil. 2016. Conversational mark- ers of constructive discussions. arXiv preprint arXiv:1604.07407 (2016)

    2016 arXiv

  142. [150]

    Siti Nur Aishah Mohd Noor and Mohd Khairul Amri Ramly. 2023. Bridging Learning Styles and Student Preferences in Construction Technology Education: VARK Model Analysis. International Journal of Academic Research in Progressive Education and Development 12, 3 (2023)

    2023

  143. [151]

    Debora Nozza, Federico Bianchi, Dirk Hovy, et al. 2021. HONEST: Measuring hurtful sentence completion in language models. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Associat...

    2021

  144. [152]

    Ahmad Oweini and Carma Daouk. 2016. Effects of the Dunn and Dunn Learning Styles Model on Reading Comprehension and Motivation: A Case Study in Innovative Learning. International Journal for Talent Development and Creativity 4 (2016), 63–70

    2016

  145. [153]

    Allan Paivio. 1990. Mental representations: A dual coding approach . Oxford university press

    1990

  146. [154]

    Allan Paivio. 2013. Imagery and verbal processes . Psychology Press

    2013

  147. [155]

    Allan Paivio, Ronald Philipchalk, and Edward J Rowe. 1975. Free and serial recall of pictures, sounds, and words. Memory & Cognition 3, 6 (1975), 586–590

    1975

  148. [156]

    John Parkinson and Jane Mansbridge. 2012. Deliberative systems: Deliberative democracy at the large scale . Cambridge University Press

    2012

  149. [157]

    Harold Pashler, Mark McDaniel, Doug Rohrer, and Robert Bjork. 2008. Learning styles: Concepts and evidence. Psychological science in the public interest 9, 3 (2008), 105–119

    2008

  150. [158]

    Jean Piaget. 1985. The Equilibration of Cognitive Structures: The Central Prob- lem of Intellectual Development

    1985

  151. [159]

    Anthony Picciano. 2009. Blending with purpose: The multimodal model.Journal of the Research Center for Educational Technology 5, 1 (2009), 4–14

    2009

  152. [160]

    Vincent Price, Joseph N Cappella, and Lilach Nir. 2002. Does disagreement contribute to more deliberative opinion? Political communication 19, 1 (2002), 95–112

    2002

  153. [161]

    John Pruitt and Tamara Adlin. 2010. The persona lifecycle: keeping people in mind throughout product design . Elsevier

    2010

  154. [162]

    David J Reid, Jianwei Zhang, and Qi Chen. 2003. Supporting scientific discovery learning in a simulation environment. Journal of Computer Assisted Learning 19, 1 (2003), 9–20

    2003

  155. [163]

    K Andrew R Richards and Michael A Hemphill. 2018. A practical guide to collaborative qualitative data analysis. Journal of Teaching in Physical education 37, 2 (2018), 225–231

    2018

  156. [164]

    Timothy B Rogers, Nicholas A Kuiper, and William S Kirker. 1977. Self-reference and the encoding of personal information. Journal of personality and social psychology 35, 9 (1977), 677

    1977

  157. [165]

    Beth A Rogowsky, Barbara M Calhoun, and Paula Tallal. 2015. Matching learning style to instructional method: Effects on comprehension. Journal of educational psychology 107, 1 (2015), 64

    2015

  158. [166]

    Michael Sankey, Dawn Birch, and Michael Gardiner. 2010. Engaging students through multimodal learning environments: The journey continues. In Proceed- ings of the 27th Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education (ASCILITE 2010)

    2010

  159. [167]

    Michael Sankey, Dawn Birch, and Michael Gardiner. 2011. The impact of multi- ple representations of content using multimedia on learning outcomes across learning styles and modal preferences. International Journal of Education and Development using ICT 7, 3 (2011), 18–35. CHI ...

    2011

  160. [168]

    Michael Sankey and Rod St Hill. 2009. The ethics of designing for multimodality: Empowering nontraditional learners. In Ethical practices and implications in distance learning. IGI Global, 125–154

    2009

  161. [169]

    Noah L Schroeder, Olusola O Adesope, and Rachel Barouch Gilbert. 2013. How effective are pedagogical agents for learning? A meta-analytic review. Journal of Educational Computing Research 49, 1 (2013), 1–39

    2013

  162. [170]

    Daniel L Schwartz and Kevin Hartman. 2014. It’s not television anymore: Designing digital video for learning and assessment. In Video research in the learning sciences. Routledge, 335–348

    2014

  163. [171]

    Neil C Schwertman, Margaret Ann Owens, and Robiah Adnan. 2004. A simple more general boxplot method for identifying outliers. Computational statistics & data analysis 47, 1 (2004), 165–174

    2004

  164. [172]

    Anuj K Shah and Daniel M Oppenheimer. 2008. Heuristics made easy: an effort-reduction framework. Psychological bulletin 134, 2 (2008), 207

    2008

  165. [173]

    Paul J Silvia. 2022. The self-reflection and insight scale: Applying item response theory to craft an efficient short form. Current Psychology 41, 12 (2022), 8635– 8645

    2022

  166. [174]

    Marion K Slack and Jolaine R Draugalis Jr. 2001. Establishing the internal and external validity of experimental studies. American journal of health-system pharmacy 58, 22 (2001), 2173–2181

    2001

  167. [175]

    Steven A Sloman. 1996. The empirical case for two systems of reasoning. Psychological bulletin 119, 1 (1996), 3

    1996

  168. [176]

    Bernd Carsten Stahl, Job Timmermans, and Brent Daniel Mittelstadt. 2016. The ethics of computing: A survey of the computing-oriented literature. Acm Computing Surveys (CSUR) 48, 4 (2016), 1–38

    2016

  169. [177]

    Marco R Steenbergen, André Bächtiger, Markus Spörndli, and Jürg Steiner

  170. [178]

    Fritz Strack and Roland Deutsch. 2004. Reflective and impulsive determinants of social behavior. Personality and social psychology review 8, 3 (2004), 220–247

    2004

  171. [179]

    Jennifer Stromer-Galley. 2007. Measuring deliberation’s content: A coding scheme. Journal of Deliberative Democracy 3, 1 (2007)

    2007

  172. [180]

    Hernawan Sulistyanto, Harun Joko Prayitno, Sutama Sutama, Sabar Narimo, and Anam Sutopo. 2023. The effectiveness of hybrid learning-based adaptive media to empower student’s critical thinking skills: Is it really for VARK learning style? Asian Journal of University Education 1...

    2023

  173. [181]

    John Sweller. 1988. Cognitive load during problem solving: Effects on learning. Cognitive science 12, 2 (1988), 257–285

    1988

  174. [182]

    Thaler, Richard H & Sunstein

    Cass R. Thaler, Richard H & Sunstein. 2009. Nudge: Improving decisions about health, wealth, and happiness . Penguin Books

    2009

  175. [183]

    Richard Thaler and Cass Sunstein. 2008. Nudge: The gentle power of choice architecture. New Haven, Conn.: Yale 10 (2008)

    2008

  176. [184]

    David Thissen, Lynne Steinberg, and Daniel Kuang. 2002. Quick and easy implementation of the Benjamini-Hochberg procedure for controlling the false positive rate in multiple comparisons. Journal of educational and behavioral statistics 27, 1 (2002), 77–83

    2002

  177. [185]

    Hobbes Thomas. 1946. Leviathan. London: Basil Blackwell

    1946

  178. [186]

    Matthias Trénel. 2004. Measuring the deliberativeness of online discussions. Coding scheme 2.4. report, Berlin: Social Science Research Centrex (2004)

    2004

  179. [187]

    Hannah M Tuller, Christopher J Bryan, Gail D Heyman, and Nicholas JS Chris- tenfeld. 2015. Seeing the other side: Perspective taking and the moderation of extremity. Journal of Experimental Social Psychology 59 (2015), 18–23

    2015

  180. [188]

    Luisa Verdoliva. 2020. Media forensics and deepfakes: an overview.IEEE journal of selected topics in signal processing 14, 5 (2020), 910–932

    2020

  181. [189]

    Anthony J Viera, Joanne M Garrett, et al. 2005. Understanding interobserver agreement: the kappa statistic. Fam med 37, 5 (2005), 360–363

    2005

  182. [190]

    Lev S Vygotsky. 1978. Mind in society: The development of higher psychological processes. Vol. 86. Harvard university press

    1978

  183. [191]

    Douglas Walton. 1999. One-sided arguments: A dialectical analysis of bias . SUNY Press

    1999

  184. [192]

    Yang Wang, Pedro Giovanni Leon, Alessandro Acquisti, Lorrie Faith Cranor, Alain Forget, and Norman Sadeh. 2014. A field trial of privacy nudges for facebook. In Proceedings of the SIGCHI conference on human factors in computing systems. 2367–2376

    2014

  185. [193]

    Lenora Delores Waters. 1972. AN ANALYSIS OF DOMINANT LEARNING MODALITIES OF SELECTED THIRD-GRADE REMEDIAL READERS IN RELA- TION TO TEACHING STRATEGIES. Baylor University

    1972

  186. [194]

    Jules White, Quchen Fu, Sam Hays, Michael Sandborn, Carlos Olea, Henry Gilbert, Ashraf Elnashar, Jesse Spencer-Smith, and Douglas C Schmidt. 2023. A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023)

    2023 arXiv

  187. [195]

    Elaine Williamson Sprague and Darren W Dahl. 2010. Learning to click: An evaluation of the personal response system clicker technology in introductory marketing courses. Journal of Marketing Education 32, 1 (2010), 93–103

    2010

  188. [196]

    Lu Xiao, Weiyu Zhang, Anna Przybylska, Anna De Liddo, Gregorio Convertino, Todd Davies, and Mark Klein. 2015. Design for online deliberative processes and technologies: Towards a multidisciplinary research agenda. In Proceedings of the 33rd Annual ACM Conference Extended Abstr...

    2015

  189. [197]

    ShunYi Yeo, Gionnieve Lim, Jie Gao, Weiyu Zhang, and Simon Tangi Perrault

  190. [198]

    Weiyu Zhang, Xiaoxia Cao, and Minh Ngoc Tran. 2013. The structural features and the deliberative quality of online discussions. Telematics and informatics 30, 2 (2013), 74–86

    2013

  191. [199]

    system role

    Weiyu Zhang, Tian Yang, and Simon Tangi Perrault. 2021. Nudge for reflection: more than just a channel to political knowledge. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems . 1–10. Multimodal Reflection Nudges for Deliberativeness CHI ’25, Apr...

    2021

  192. [1995]

    The Journal of Educational Research 88, 6 (1995), 353–362

    A meta-analytic validation of the Dunn and Dunn model of learning-style preferences. The Journal of Educational Research 88, 6 (1995), 353–362

    1995

  193. [2003]

    Comparative European Politics 1 (2003), 21–48

    Measuring political deliberation: A discourse quality index. Comparative European Politics 1 (2003), 21–48

    2003

  194. [2005]

    Computers in human behavior 21, 4 (2005), 555–573

    Multimedia learning in social sciences: Limitations of external graphical representations. Computers in human behavior 21, 4 (2005), 555–573

    2005

  195. [2019]

    In Proceedings of the 2019 CHI conference on human factors in computing systems

    23 ways to nudge: A review of technology-mediated nudging in human- computer interaction. In Proceedings of the 2019 CHI conference on human factors in computing systems. 1–15

    2019

  196. [2022]

    arXiv preprint arXiv:2203.07785 (2022)

    The Ghost in the Machine has an American accent: value conflict in GPT-3. arXiv preprint arXiv:2203.07785 (2022)

    2022 arXiv

  197. [2023]

    ACM Transactions on Computer-Human Interaction 31, 1 (2023), 1–38

    CoAIcoder: Examining the effectiveness of AI-assisted human-to-human collaboration in qualitative analysis. ACM Transactions on Computer-Human Interaction 31, 1 (2023), 1–38

    2023

  198. [2024]

    In Proceedings of the CHI Conference on Human Factors in Computing Systems

    Help Me Reflect: Leveraging Self-Reflection Interface Nudges to Enhance Deliberativeness on Online Deliberation Platforms. In Proceedings of the CHI Conference on Human Factors in Computing Systems . 1–32

Pith tools

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