Educational Psychology Review

, Volume 25, Issue 4, pp 445–472 | Cite as

Designing Instructional Text in a Conversational Style: A Meta-analysis

Review Article

Abstract

This article reviews research on the effects of conversational style on learning. Studies of conversational style have variously investigated “personalization” through changing instances of first-person address to second or third person, including sentences that directly address the learner; including more polite forms of address; and making the views and personality of the author more visible. Meta-analyses provided mixed support for a model of learning processes; statistically reliable average effects were found on self-reports of friendliness (d = 0.46) and effective cognitive processing (d = 0.62), but not learning assistance (d = 0.16) and interest (d = 0.15). Statistically reliable average effects on retention (d = 0.30) and transfer (d = 0.54) learning outcomes supported conversational-style redesigns across a range of potential moderators; the clearest apparent boundary condition for learning outcomes across the moderators under analysis was instructional time, with small, non-significant effects being found in studies longer than 35 min. Recommendations for future investigations are discussed.

Keywords

Conversational style Personalization Instructional design Meta-analysis 

References

  1. Ainley, M., Corrigan, M., & Richardson, N. (2005). Students, tasks and emotions: identifying the contribution of emotions to students’ reading of popular culture and popular science texts. Learning and Instruction, 15, 433–447. doi:10.1016/j.learninstruc.2005.07.011.CrossRefGoogle Scholar
  2. Ainley, M., Hidi, S., & Berndorff, D. (2002). Interest, learning and the psychological processes that mediate their relationship. Journal of Educational Psychology, 94, 545–561. doi:10.1037//0022-0663.94.3.545.CrossRefGoogle Scholar
  3. Anderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruikshank, K. A., Mayer, R. E., Pintrich, P. R., et al. (2001). A taxonomy for learning, teaching, and assessing: a revision of Bloom’s taxonomy of educational objectives. New York: Longman.Google Scholar
  4. Antheunis, M. L., Schouten, A. P., Valkenburg, P. M., & Peter, J. (2012). Interactive uncertainty reduction strategies and verbal affection in computer-mediated communication. Communication Research, 39, 757–780. doi:10.1177/0093650211410420.CrossRefGoogle Scholar
  5. Bargiela-Chiappini, F., & Kadar, D. (Eds.). (2010). Politeness across cultures. Basingstoke: Palgrave.Google Scholar
  6. Bereiter, C., & Scardamalia, M. (1987). The psychology of written composition. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  7. Bretzing, B. H., & Kulhavy, R. W. (1981). Note-taking and passage style. Journal of Educational Psychology, 73, 242–250.CrossRefGoogle Scholar
  8. Brown, P., & Levinson, S. C. (1987). Politeness: some universals in language use. New York, NY: Cambridge University Press.Google Scholar
  9. Cohen, J. (1983). The cost of dichotomization. Applied Psychological Measurement, 7, 249–253.CrossRefGoogle Scholar
  10. Clarebout, G., & Elen, J. (2007). In search of pedagogical agents’ modality and dialogue effects in open learning environments. e-Journal of Instructional Science and Technology (e-JIST), 10. Retrieved 3 September 2010 from http://www.ascilite.org.au/ajet/e-jist/docs/vol10_no1/papers/full_papers/clarebout_elen.htm.
  11. D’Ailly, H. H., Murray, H. G., & Corkill, A. (1995). Cognitive effects of self-referencing. Contemporary Educational Psychology, 20, 88–113.CrossRefGoogle Scholar
  12. Doolittle, P. (2010). The effects of segmentation and personalization on superficial and comprehensive strategy instruction in multimedia learning environments. Journal of Educational Multimedia and Hypermedia, 19, 159–175.Google Scholar
  13. Dunsworth, Q. (2005). Fostering multimedia learning of science: the role of personalization and presentation mode. Doctoral dissertation. Available from ProQuest Dissertations and Theses database. UMI no. AAT 3173234.Google Scholar
  14. Field, A. P., & Gillett, R. (2010). How to do a meta-analysis. British Journal of Mathematical and Statistical Psychology, 63, 665–694. doi:10.1348/000711010X502733.CrossRefGoogle Scholar
  15. Ginns, P., & Fraser, J. (2010). Personalization enhances learning anatomy terms. Medical Teacher, 32, 776–778. doi:10.3109/01421591003692714.CrossRefGoogle Scholar
  16. Graesser, A. C., Hauft-Smith, K., Cohen, A. D., & Pyles, L. D. (1980a). Advanced outlines, familiarity, text genre, and retention of prose. The Journal of Experimental Education, 48, 209–220.Google Scholar
  17. Graesser, A. C., Hoffman, N. L., & Clark, L. F. (1980b). Structural components of reading time. Journal of Verbal Learning and Verbal Behavior, 19, 131–151.Google Scholar
  18. Graesser, A. C., Olde, B., & Klettke, B. (2002). How does the mind construct and represent stories? In M. C. Green, J. J. Strange, & T. C. Brock (Eds.), Narrative impact: social and cognitive foundations (pp. 231–263). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  19. Graesser, A. C., & Ottati, V. (1996). Why stories? Some evidence, questions, and challenges. In R. S. Wyer (Ed.), Knowledge and memory: the real story (pp. 121–132). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  20. Grice, H. P. (1975). Logic and conversation. In P. Cole & J. L. Morgan (Eds.), Syntax and semantics 3: Speech arts (pp. 41–58). New York, NY: Academic.Google Scholar
  21. Haberlandt, K., & Graesser, A. C. (1985). Component processes in text comprehension and some of their interactions. Journal of Experimental Psychology. General, 114, 357–374.CrossRefGoogle Scholar
  22. Hattie, J. (2009). Visible learning: a synthesis of over 800 meta-analyses relating to achievement. New York, NY: Routledge.Google Scholar
  23. Hayes, A. F., & Matthes, J. (2009). Computational procedures for probing interactions in OLS and logistic regression: SPSS and SAS implementations. Behavior Research Methods, 41, 924–936. doi:10.3758/BRM.41.3.924.CrossRefGoogle Scholar
  24. Hedges, L. V., & Olkin, I. (1985). Statistical methods for meta-analysis. New York: Academic Press.Google Scholar
  25. Hedges, L. V., & Vevea, J. L. (1998). Fixed- and random-effects models in meta-analysis. Psychological Methods, 3, 486–504. doi:10.1037/1082-989X.3.4.486.CrossRefGoogle Scholar
  26. Hunter, J. E., & Schmidt, F. L. (2000). Fixed effects vs. random effects meta-analysis methods: Implications for cumulative research knowledge. International Journal of Selection and Assessment, 8, 275-292. doi:10.1111/1468-2389.00156.
  27. Inglese, T., Mayer, R. E., & Rigotti, F. (2007). Using audiovisual TV interviews to create visible authors that reduce the learning gap between native and non-native speakers. Learning and Instruction, 17, 67–77. doi:10.1016/j.learninstruc.2006.11.006.CrossRefGoogle Scholar
  28. Johnson, C. I., & Mayer, R. E. (2012). An eye movement analysis of the spatial contiguity effect in multimedia learning. Journal of Experimental Psychology: Applied, 18, 178–191. doi:10.1037/a0026923.CrossRefGoogle Scholar
  29. Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19, 509–539. doi:10.1007/s10648-007-9054-3.CrossRefGoogle Scholar
  30. Kartal, G. (2007). How universal are e-learning design guidelines? Reconsidering the personalization principle. In D. Remenyi (Ed.), Proceedings of the 2nd International Conference on E-Learning (pp. 269–275). Reading, UK: Academic Conferences.Google Scholar
  31. Kartal, G.. (2010). Does language matter in multimedia learning? Personalization principle revisited. Journal of Educational Psychology, 102, 615–624. doi:10.1037/a0019345.Google Scholar
  32. Lorch, R. F. (1989). Text-signaling devices and their effects on reading and memory processes. Educational Psychology Review, 1, 209–234. doi:10.1007/BF01320135.CrossRefGoogle Scholar
  33. MacCallum, R.C., Zhang, S.,. Preacher, K.J., & Rucker, D.D. (2002). On the practice of dichotomization of quantitative variables. Psychological Methods, 7, 19-40. doi:10.1037//1082-989X.7.1.19.
  34. Mayer, R. E. (2005a). Principles of multimedia learning based on social cues: personalization, voice, and image principles. In R. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 201–212). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  35. Mayer, R. E. (2008). Applying the science of learning: evidence-based principles for the design of multimedia instruction. American Psychologist, 63, 760–769. doi:10.1037/0003-066X.63.8.770b.CrossRefGoogle Scholar
  36. Mayer, R. E. (2001). Multimedia learning (1st ed.). New York, NY: Cambridge University Press.CrossRefGoogle Scholar
  37. Mayer, R. E. (2005b). Principles of multimedia learning based on social cues: personalization, voice, and image principles. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 201–212). New York, NY: Cambridge University Press.CrossRefGoogle Scholar
  38. Mayer, R. E. (2009). Multimedia learning (2nd ed.). New York, NY: Cambridge University Press.CrossRefGoogle Scholar
  39. Mayer, R. E., Fennell, S., Farmer, L., & Campbell, J. (2004). A personalization effect in multimedia learning: students learn better when words are in conversational style rather than formal style. Journal of Educational Psychology, 96, 389–395. doi:10.1037//1082-989X.7.1.19.CrossRefGoogle Scholar
  40. Mayer, R. E., Johnson, W. L., Shaw, E., & Sandhu, S. (2006). Constructing computer-based tutors that are socially sensitive: politeness in educational software. International Journal of Human Computer Studies, 64, 36–42. doi:10.1016/j.ijhcs.2005.07.001.CrossRefGoogle Scholar
  41. McLaren, B. M., DeLeeuw, K. E., & Mayer, R. E. (2011a). Polite web-based intelligent tutors: can they improve learning in classrooms? Computers & Education, 56, 574–584. doi:10.1016/j.compedu.2010.09.019.CrossRefGoogle Scholar
  42. McLaren, B. M., DeLeeuw, K. E., & Mayer, R. E. (2011b). A politeness effect in learning with web-based intelligent tutors. International Journal of Human-Computer Studies, 69, 70–79.CrossRefGoogle Scholar
  43. McLaren, B. M., Lim, S., Gagnon, F., Yaron, D., & Koedinger, K. R. (2006). Studying the effects of personalized language and worked examples in the context of a web-based intelligent tutor. Proceedings of the 8th International Conference on Intelligent Tutoring Systems, Jhongli, Taiwan, June 26–30, 2006.Google Scholar
  44. McLaren, B.M., Lim, S., Yaron, D., & Koedinger, K.R. (2007). Can a polite intelligent tutoring system lead to improved learning outside of the lab? In R. Luckin, K.R. Koedinger, & J. Greer (Eds.), Proceedings of the 13th International Conference on Artificial Intelligence in Education (AIED-07), Artificial Intelligence in Education: Building Technology Rich Learning Contexts that Work (pp. 433–440). Amsterdam: IOS Press.Google Scholar
  45. Menke, D. J., & Pressley, M. (1994). Elaborative interrogation: using “why” questions to enhance the learning from text. Journal of Reading, 37, 642–645. http://www.jstor.org/stable/20172385.Google Scholar
  46. Moreno, R., & Mayer, R. E. (2000). Engaging students in active learning: the case for personalized multimedia messages. Journal of Educational Psychology, 92, 724–733. doi:10.1037/0022-0663.92.4.724.CrossRefGoogle Scholar
  47. Moreno, R., & Mayer, R. E. (2004). Personalized messages that promote science learning in virtual environments. Journal of Educational Psychology, 96, 165–173. doi:10.1037/0022-0663.96.1.165.CrossRefGoogle Scholar
  48. Nolen, S. B. (1995). Effects of a visible author in statistics texts. Journal of Educational Psychology, 87, 47–65.CrossRefGoogle Scholar
  49. Paxton, R. J. (1997). “Someone with like a life wrote it”: the effects of a visible author on high school history students. Journal of Educational Psychology, 89, 235–250.CrossRefGoogle Scholar
  50. Paxton, R. J. (1999). A deafening silence: history textbooks and the students who read them. Review of Educational Research, 69, 315–339. doi:10.3102/00346543069003315.CrossRefGoogle Scholar
  51. Paxton, R. J. (2002). The influence of author visibility on high school students solving a historical problem. Cognition and Instruction, 20, 197–248.CrossRefGoogle Scholar
  52. Symons, C., & Johnson, B. (1997). The self-reference effect in memory: a meta-analysis. Psychological Bulletin, 121, 371–394.CrossRefGoogle Scholar
  53. Ramirez, A., Walther, J. B., Burgoon, J. K., & Sunnafrank, M. (2002). Information-seeking strategies, uncertainty, and computer-mediated communication: toward a conceptual model. Human Communication Research, 28, 213–228. doi:10.1111/j.1468-2958.2002.tb00804.x.Google Scholar
  54. Reeves, B., & Nass, C. (1996). The media equation: how people treat computers, television, and new media like real people and places. New York, NY: Cambridge University Press.Google Scholar
  55. Robertson, E. (2008). Effects of personalising anatomy instructions on learning. Honours thesis, The University of Sydney.Google Scholar
  56. Son, J.Y., & Goldstone, R.L. (2009). Contextualization in perspective. Cognition and Instruction, 27, 51–89. doi:10.1080/07370000802584539.Google Scholar
  57. Spiro, R. J. (1977). Remembering information from text: the “state of schema” approach. In R. C. Anderson, R. J. Spiro, & W. E. Montague (Eds.), Schooling and the acquisition of knowledge (pp. 137–165). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  58. Stiller, K. D., & Jedlicka, R. (2010). A kind of expertise reversal effect: personalization effect can depend on domain-specific prior knowledge. Australasian Journal of Educational Technology, 26, 133–149.Google Scholar
  59. Schworm, S., & Stiller, K. D. (2012). Does personalization matter? The role of social cues in instructional explanations. Intelligent Decision Technologies, 6, 105–111. doi:10.3233/IDT-2012-0127.Google Scholar
  60. Tidwell, L. C., & Walther, J. B. (2002). Computer-mediated communication effects on disclosure, impressions, and interpersonal evaluations: getting to know one another a bit at a time. Human Communication Research, 28, 17–348.CrossRefGoogle Scholar
  61. Vevea, J. L., & Woods, C. M. (2005). Publication bias in research synthesis: sensitivity analysis using a priori weight functions. Psychological Methods, 10, 428–443. doi:10.1037/1082-989X.10.4.428.CrossRefGoogle Scholar
  62. Wagner, L., Davis, S., & Handelsman, M. M. (1998). In search of the abominable consent form: the impact of readability and personalization. Journal of Clinical Psychology, 54, 115–120. doi:10.1002/%28SICI%291097-4679%28199801%2954:1%3C115::AID-JCLP13%3E3.0.CO;2-N.CrossRefGoogle Scholar
  63. Walther, J. B. (1992). Interpersonal effects in computer-mediated interaction: a relational perspective. Communication Research, 19, 52–90. doi:10.1177/009365092019001003.CrossRefGoogle Scholar
  64. Wang, N., & Johnson, W. L. (2008). The politeness effect in an intelligent foreign language tutoring system. In B. P. Woolf, E. Aimeur, R. Nkambou, and Lajoie, S. (Eds.), Proceedings of the Ninth International Conference on Intelligent Tutoring Systems. Lecture Notes in Computer Science, vol. 5091 (pp. 260–280). Berlin: Springer.Google Scholar
  65. Wang, N., Johnson, W.L., Mayer, R.E., Rizzo, P., Shaw, E., & Collins, H. (2008). The politeness effect: pedagogical agents and learning outcomes. International Journal of Human–Computer Studies, 66, 98–112. doi:10.1016/j.ijhcs.2007.09.003 Google Scholar
  66. Wilson, D.B. (2010). SPSS, Stata, and SAS macros for performing meta-analytic analyses. Retrieved September 19, 2012 from http://mason.gmu.edu/∼dwilsonb/ma.html.
  67. Yeung, A., Schmid, S., George, A. V., & King, M. M. (2009). Using the personalization hypothesis to design e-learning environments. In M. Gupta-Bhowon, S. Jhaumeer-Laulloo, H. L. K. Wah, & P. Ramasami (Eds.), Chemistry education in the ICT Age (pp. 287–300). Berlin: Springer. doi:10.1007/978-1-4020-9732-4_25.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Paul Ginns
    • 1
  • Andrew J. Martin
    • 1
  • Herbert W. Marsh
    • 2
  1. 1.Faculty of Education and Social WorkThe University of SydneySydneyAustralia
  2. 2.University of Western SydneySydneyAustralia

Personalised recommendations