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Informational Environments: Cognitive, Motivational-Affective, and Social-Interactive Forays into the Digital Transformation

  • Jürgen Buder
  • Friedrich W. Hesse
Chapter

Abstract

This chapter deals with the question of how people use digital technologies in order to get a better understanding of the world surrounding them, thus offering a cognitively inspired perspective to the current digital transformation of society. After delineating the importance of information and information processing, we introduce the concept of informational environments as the set of informational resources that a person habitually taps into over a life-course in order to get a better understanding of the world. Implications of this definition are discussed. We also introduce the two main themes of the current book and provide a preview on the subsequent chapters. The first main theme (effects of use) is to provide descriptive accounts of how people currently make use of their informational environments. On the one hand, effects of use depend on cognitive, motivational-affective, and social-interactive characteristics of a person. On the other hand, effects of use depend on the characteristics of the environment (e.g., rules and regulations). The second main theme (effective designs) prescriptively refers to the way that informational resources in an informational environment can be designed to improve learning. It is argued that digital technologies can provide such learning opportunities inasmuch as they engender teacher-like characteristics. Among these are cognitive characteristics (e.g., fostering self-regulation skills), motivational-affective characteristics (e.g., fostering emotion regulation), and social-interactive characteristics (e.g., providing interactive support that is adapted to the current needs of a learner).

Keywords

Informational environments Learning Digital technologies Self-regulated learning Attitudes Adaptive learning systems 

References

  1. Ainsworth, S. (2008). How do animations influence learning? In D. H. Robinson & G. J. Schraw (Eds.), Recent innovations in educational technology that facilitate student learning(pp. 37–67). Charlotte, NC: Information Age.Google Scholar
  2. Aleven, V., Mclaren, B. M., Sewall, J., & Koedinger, K. R. (2009). A new paradigm for intelligent tutoring systems: Example-tracing tutors. International Journal of Artificial Intelligence in Education, 19, 105–154.Google Scholar
  3. Anderson, J. R. (1983). The architecture of cognition. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  4. Asch, S. E. (1951). Effects of group pressure on the modification and distortion of judgments. In H. Guetzkow (Ed.), Groups, leadership and men (pp. 177–190). Pittsburgh, PA: Carnegie Press.Google Scholar
  5. Azevedo, R., Millar, G. C., Taub, M., Mudrick, N. V., Bradbury, A. E., & Price, M. J. (2017). Using data visualizations to foster emotion regulation during self-regulated learning with advanced learning technologies. In J. Buder & F. W. Hesse (Eds.), Informational environments: Effects of use, effective designs (pp. 225–247). New York, NY: Springer.Google Scholar
  6. Baddeley, A. (2007). Working memory, thought, and action. Oxford: University Press.CrossRefGoogle Scholar
  7. Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White (Eds.), Learning analytics (pp. 61–75). New York, NY: Springer.Google Scholar
  8. Banaji, M. R., & Prentice, D. A. (1994). The self in social contexts. Annual Review of Psychology, 45, 297–332.CrossRefGoogle Scholar
  9. Bartholomé, T., & Bromme, R. (2009). Coherence formation when learning from text and pictures: What kind of support for whom? Journal of Educational Psychology, 101, 282–293.CrossRefGoogle Scholar
  10. Baumeister, R. F., & Leary, M. R. (1995). The need to belong: desire for interpersonal attachments as a fundamental human motivation. Psychological Bulletin, 117, 497–529.CrossRefPubMedGoogle Scholar
  11. Boekaerts, M. (1999). Self-regulated learning: Where we are today. International Journal of Educational Research, 31, 445–457.CrossRefGoogle Scholar
  12. Brusilovsky, P., & Peylo, C. (2003). Adaptive and intelligent web-based educational systems. International Journal of Artificial Intelligence in Education, 13, 159–172.Google Scholar
  13. Buder, J. (2017). A conceptual framework of knowledge exchange. In S. Schwan & U. Cress (Eds.), The psychology of digital learning: Constructing, exchanging, and acquiring knowledge with digital media (pp. 105–122). Cham, Switzerland: Springer International Publishing.CrossRefGoogle Scholar
  14. Buder, J., Buttliere, B., & Cress, U. (2017). The role of cognitive conflicts in informational environments: Conflicting evidence from the learning sciences and social psychology? In J. Buder & F. W. Hesse (Eds.), Informational environments: Effects of use, effective designs (pp. 53–74). New York, NY: Springer.Google Scholar
  15. Buder, J., & Hesse, F. W. (2016). Designing digital technologies for deeper learning. In M. Spector, B. B. Lockee, & M. Childress (Eds.), Learning, design, and technology: An international compendium of theory, research, practice, and policy. New York, NY: Springer.Google Scholar
  16. Buder, J., & Schwind, C. (2012). Learning with personalized recommender systems: A psychological view. Computers in Human Behavior, 28, 207–216.CrossRefGoogle Scholar
  17. Campbell, W. K., & Sedikides, C. (1999). Self-threat magnifies the self-serving bias: A meta-analytic integration. Review of General Psychology, 3, 23–43.CrossRefGoogle Scholar
  18. Chandler, P., & Sweller, J. (1992). The split-attention effect as a factor in the design of instruction. British Journal of Educational Psychology, 62, 233–246.CrossRefGoogle Scholar
  19. Clark, R. E., & Voogel, A. (1985). Transfer of training principles for instructional design. ECTJ, 33, 113.Google Scholar
  20. Cress, U., & Kimmerle, J. (2008). A systemic and cognitive view on collaborative knowledge building with wikis. International Journal of Computer-Supported Collaborative Learning, 3, 105–122.CrossRefGoogle Scholar
  21. Cress, U., Moskaliuk, J., & Jeong, H. (Eds.). (2016). Mass collaboration and education (Vol. 16). Cham: Springer International Publishing.Google Scholar
  22. D’Mello, S. (2013). A selective meta-analysis on the relative incidence of discrete affective states during learning with technology. Journal of Educational Psychology, 105, 1082–1099.CrossRefGoogle Scholar
  23. dscout (2016, June 15). Mobile touches: dscout’s inaugural study on humans and their tech. Retrieved from https://blog.dscout.com/mobile-touches
  24. Engelmann, T., Dehler, J., Bodemer, D., & Buder, J. (2009). Knowledge awareness in CSCL: A psychological perspective. Computers in Human Behavior, 25, 949–960.CrossRefGoogle Scholar
  25. Festinger, L. (1957). A theory of cognitive dissonance. Evanston, IL: Row, Peterson.Google Scholar
  26. Fischer, G. (2000). Lifelong learning—More than training. Journal of Interactive Learning Research, 11, 265–294.Google Scholar
  27. Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 34, 906–911.CrossRefGoogle Scholar
  28. Fu, W.-T., & Pirolli, P. (2007). SNIF-ACT: A cognitive model of user navigation on the World Wide Web. Human-Computer Interaction, 22, 355–412.Google Scholar
  29. Graesser, A. C., Lippert, A. M., & Hampton, A. J. (2017). Successes and failures in building learning environments to promote deep learning: The value of conversational agents. In J. Buder & F. W. Hesse (Eds.), Informational environments: Effects of use, effective designs (pp. 273–298). New York, NY: Springer.Google Scholar
  30. Hart, W., Albarracín, D., Eagly, A. H., Brechan, I., Lindberg, M. J., & Merrill, L. (2009). Feeling validated versus being correct: A meta-analysis of selective exposure to information. Psychological Bulletin, 135, 555–588.CrossRefPubMedPubMedCentralGoogle Scholar
  31. Hillmert, S., Groß, M., Schmidt-Hertha, B., & Weber, H. (2017). Informational environments and college student dropout. In J. Buder & F. W. Hesse (Eds.), Informational environments: Effects of use, effective designs (pp. 27–52). New York, NY: Springer.Google Scholar
  32. Höller, J., Tsiatsis, V., Mulligan, C., Karnouskos, S., Avesand, S., & Boyle, D. (2014). From machine-to-machine to the Internet of things: Introduction to a new age of intelligence. Amsterdam: Elsevier.Google Scholar
  33. Hutchins, E. (1995). Cognition in the wild. Cambridge, MA: MIT Press.Google Scholar
  34. Johnson, D., & Johnson, R. (1975). Learning together and alone: Cooperative, competitive, and individualistic learning. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  35. Kalyuga, S., & Sweller, J. (2005). Rapid dynamic assessment of expertise to improve the efficiency of adaptive e-learning. Educational Technology Research and Development, 53, 83–93.CrossRefGoogle Scholar
  36. Kimmerle, J., Bientzle, M., Cress, U., Flemming, D., Greving, H., Grapendorf, J., … Sassenberg, K. (2017). Motivated processing of health-related information in online environments. In J. Buder & F. W. Hesse (Eds.), Informational environments: Effects of use, effective designs (pp. 75–96). New York, NY: Springer.Google Scholar
  37. Koedinger, K., & Corbett, A. (2006). Cognitive tutors: Technology bringing learning science to the classroom. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 61–78). Cambridge, MA: University Press.Google Scholar
  38. Kollar, I., Fischer, F., & Hesse, F. W. (2006). Collaboration scripts—A conceptual analysis. Educational Psychology Review, 18, 159–185.CrossRefGoogle Scholar
  39. Kunda, Z. (1990). The case for motivated reasoning. Psychological Bulletin, 108, 480–498.CrossRefPubMedGoogle Scholar
  40. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge, MA: Cambridge University Press.CrossRefGoogle Scholar
  41. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.CrossRefPubMedGoogle Scholar
  42. Lord, C. G., Ross, L., & Lepper, M. R. (1979). Biased assimilation and attitude polarization: The effects of prior theories on subsequently considered evidence. Journal of Personality and Social Psychology, 37, 2098–2109.CrossRefGoogle Scholar
  43. Markoff, J. (2016). Machines of loving grace: The quest for common ground between humans and robots. New York, NY: Harper Collins.Google Scholar
  44. Marslen-Wilson, W. D., & Welsh, A. (1978). Processing interactions and lexical access during word recognition in continuous speech. Cognitive Psychology, 10, 29–63.CrossRefGoogle Scholar
  45. Mason, L., Pluchino, P., & Tornatora, M. C. (2015). Eye-movement modeling of integrative reading of an illustrated text: Effects on processing and learning. Contemporary Educational Psychology, 41, 172–187.CrossRefGoogle Scholar
  46. Mayer, R. E. (Ed.). (2005). The Cambridge handbook of multimedia learning. Cambridge, MA: University Press.Google Scholar
  47. McNamara, J. (1982). Optimal patch use in a stochastic environment. Theoretical Population Biology, 21, 269–288.CrossRefGoogle Scholar
  48. Melton, A. W. (1963). Implications of short-term memory for a general theory of memory. Journal of Verbal Learning and Verbal Behavior, 2, 1–21.CrossRefGoogle Scholar
  49. Meyer, D. E., & Schvaneveldt, R. W. (1971). Facilitation in recognizing pairs of words: Evidence of a dependence between retrieval operations. Journal of Experimental Psychology, 90, 227–234.CrossRefPubMedGoogle Scholar
  50. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81–97.CrossRefPubMedGoogle Scholar
  51. Miller, G. A. (1983). Informavores. In F. Machlup & U. Mansfield (Eds.), The study of information: Interdisciplinary messages (pp. 111–113). New York, NY: Wiley.Google Scholar
  52. Murata, A. (2005). An attempt to evaluate mental workload using wavelet transform of EEG. Human Factors, 47, 498–508.CrossRefPubMedGoogle Scholar
  53. Nelson, T. O., & Narens, L. (1994). Why investigate metacognition? In J. Metcalfe & A. Shimamura (Eds.), Metacognition: Knowing about knowing (pp. 1–25). Cambridge, MA: MIT Press.Google Scholar
  54. Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  55. Olson, J. M., & Zanna, M. P. (1993). Attitudes and attitude change. Annual Review of Psychology, 44, 117–154.CrossRefGoogle Scholar
  56. Piaget, J. (1970). Piaget’s theory. In P. H. Mussen (Ed.), Carmichael’s manual of child psychology (Vol. 1, pp. 703–732). New York, NY: Wiley.Google Scholar
  57. Pirolli, P., & Card, S. (1999). Information foraging. Psychological Review, 106, 643–675.CrossRefGoogle Scholar
  58. Plass, J. L., O’Keefe, P. A., Homer, B. D., Case, J., Hayward, E. O., Stein, M., & Perlin, K. (2013). The impact of individual, competitive, and collaborative mathematics game play on learning, performance, and motivation. Journal of Educational Psychology, 105, 1050–1066.CrossRefGoogle Scholar
  59. Pressey, S. L. (1926). A simple apparatus which gives tests and scores—And teaches. School and Society, 23, 373–376.Google Scholar
  60. Renkl, A. (1997). Learning from worked-out examples: A study on individual differences. Cognitive Science, 21, 1–29.CrossRefGoogle Scholar
  61. Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33, 135–146.CrossRefGoogle Scholar
  62. Roscoe, R. D ., & Chi, M. T. H. (2007). Understanding tutor learning: Knowledge-building and knowledge-telling in peer tutor's explanations and questions. Review of Education Research, 77, 534–574.Google Scholar
  63. Rosenberg, M. J., & Hovland, C. I. (1960). Cognitive, affective, and behavioral components of attitudes. In C. I. Hovland & M. J. Rosenberg (Eds.), Attitude organization and change: An analysis of consistency among attitude components (pp. 1–14). New Haven, CT: Yale University Press.Google Scholar
  64. Scardamalia, M., & Bereiter, C. (2006). Knowledge building: Theory, pedagogy, and technology. In K. Sawyer (Ed.), Cambridge handbook of the learning sciences (pp. 97–118). New York, NY: Cambridge University Press.Google Scholar
  65. Scheiter, K., Fillisch, B., Krebs, M.-C., Leber, J., Plötzer, R., Renkl, A., … Zimmermann, G. (2017). How to design adaptive multimedia environments to support self-regulated learning. In J. Buder & F. W. Hesse (Eds.), Informational environments: Effects of use, effective designs (pp. 203–223). New York, NY: Springer.Google Scholar
  66. Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46, 30–32.Google Scholar
  67. Skinner, B. F. (1961). Why we need teaching machines. Harvard Educational Review, 31, 377–398.Google Scholar
  68. Soltanlou, M., Jung, S., Roesch, S., Ninaus, M., Brandelik, K., Heller, J., … Moeller, K. (2017). Behavioral and neurocognitive evaluation of a web-platform for game-based learning of orthography and numeracy. In J. Buder & F. W. Hesse (Eds.), Informational environments: Effects of use, effective designs (pp. 149–176). New York, NY: Springer.Google Scholar
  69. Spiro, R. J., Collins, B. P., Thota, J. J., & Feltovich, P. J. (2003). Cognitive flexibility theory: Hypermedia for complex learning, adaptive knowledge application, and experience acceleration. Educational Technology, 43, 5–10.Google Scholar
  70. Spüler, M., Krumpe, T., Walter, C., Scharinger, C., Rosenstiel, W., & Gerjets, P. (2017). Brain-computer interfaces for educational applications. In J. Buder & F. W. Hesse (Eds.), Informational environments: Effects of use, effective designs (pp. 177–201). New York, NY: Springer.Google Scholar
  71. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285.CrossRefGoogle Scholar
  72. Sweller, J., Van Merriënboer, J. J., & Paas, F. G. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–296.CrossRefGoogle Scholar
  73. Turner, J. C., & Oakes, P. J. (1986). The significance of the social identity concept for social psychology with reference to individualism, interactionism and social influence. British Journal of Social Psychology, 25, 237–252.CrossRefGoogle Scholar
  74. van Gog, T., & Scheiter, K. (2010). Eye tracking as a tool to study and enhance multimedia learning. Learning and Instruction, 20, 95–99.CrossRefGoogle Scholar
  75. VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46, 197–221.CrossRefGoogle Scholar
  76. Voss, T., Goeze, A., Marx, C., Hoehne, V., Klotz, V., & Schrader, J. (2017). Using digital media to assess and promote school and adult education teacher competence. In J. Buder & F. W. Hesse (Eds.), Informational environments: Effects of use, effective designs (pp. 125–148). New York, NY: Springer.Google Scholar
  77. Werquin, P. (2010, February). Recognition of non-formal and informal learning: Country practices. Paris: OECD.Google Scholar
  78. Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277–304). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  79. Winne, P. H., Vytasek, J. M., Patzak, A., Rakovic, M., Marzouk, Z., Pakdaman-Savoji, A., … Nesbit, H. C. (2017). Designs for learning analytics to support information problem solving. In J. Buder & F. W. Hesse (Eds.), Informational environments: Effects of use, effective designs (pp. 249–272). New York, NY: Springer.Google Scholar
  80. Witzany, G. (Ed.). (2014). Biocommunication of animals. Dordrecht: Springer.Google Scholar
  81. Wood, W., Lundgren, S., Ouellette, J. A., Busceme, S., & Blackstone, T. (1994). Minority influence: A meta-analytic review of social influence processes. Psychological Bulletin, 115, 323–345.CrossRefPubMedGoogle Scholar
  82. Zurstiege, G., Zipfel, S., Ort, A., Mack, I., Meitz, T. G. K., & Schäffeler, N. (2017). Managing obesity prevention using digital media—A double-sided approach. In J. Buder & F. W. Hesse (Eds.), Informational environments: Effects of use, effective designs (pp. 97–123). New York, NY: Springer.Google Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Leibniz-Institut für Wissensmedien (IWM)TübingenGermany

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