E-learning (theory)

E-learning theory describes the cognitive science principles of effective multimedia learning using electronic educational technology.[1][2][3] Cognitive research and theory suggest that selection of appropriate concurrent multimedia modalities may enhance learning, as may application of several other principles.

Multimedia instructional design principles

Main article: Modality effect

Beginning with cognitive load theory as their motivating scientific premise, researchers such as Richard E. Mayer, John Sweller, and Roxana Moreno established within the scientific literature a set of multimedia instructional design principles that promote effective learning.[4][5][6] Many of these principles have been "field tested" in everyday learning settings and found to be effective there as well.[7][8][9] The majority of this body of research has been performed using university students given relatively short lessons on technical concepts with which they held low prior knowledge. However, a number of studies have shown these principles to be effective with learners of other ages and with non-technical learning content.[10][11] Research using learners who have greater prior knowledge in the lesson material sometimes finds results that contradict these design principles. This has led some researchers to put forward the "expertise effect" as an instructional design principle unto itself.[12][13][14][15]

The underlying theoretical premise, cognitive load theory, describes the amount of mental effort that is related to performing a task as falling into one of three categories: germane, intrinsic, and extraneous.[16] Germane cognitive load is the mental effort required to process the task's information, make sense of it, and access and/or store it in long-term memory (for example, seeing a math problem, identifying the values and operations involved, and understanding that your task is to solve the math problem). Intrinsic cognitive load is the mental effort required to perform the task itself (for example, actually solving the math problem). Extraneous cognitive load is the mental effort imposed by the way that the task is delivered, which may or may not be efficient (for example, finding the math problem you are supposed to solve on a page that also contains advertisements for books about math).

The multimedia instructional design principles identified by Mayer, Sweller, Moreno, and their colleagues are largely focused on minimizing extraneous cognitive load, and managing intrinsic and germane loads at levels that are appropriate for the learner. Examples of these principles in practice include

Cognitive load theory (and by extension many of the multimedia instructional design principles) is based in part on a model of working memory by Alan Baddeley and Graham Hitch who proposed that working memory has two largely independent, limited capacity sub-components that tend to work in parallel – one visual and one verbal/acoustic.[24] This gave rise to dual-coding theory, first proposed by Allan Paivio and later applied to multimedia learning by Richard Mayer. According to Mayer,[3] separate channels of working memory process auditory and visual information during any lesson. Consequently, a learner can use more cognitive processing capacities to study materials that combine auditory verbal information with visual graphical information than to process materials that combine printed (visual) text with visual graphical information. In other words, the multi-modal materials reduce the cognitive load imposed on working memory.

In a series of studies Mayer and his colleagues tested Paivio's dual-coding theory, with multimedia lesson materials. They repeatedly found that students given multimedia with animation and narration consistently did better on transfer questions than those who learn from animation and text-based materials. That is, they were significantly better when it came to applying what they had learned after receiving multimedia rather than mono-media (visual only) instruction. These results were then later confirmed by other groups of researchers.

The initial studies of multimedia learning were limited to logical scientific processes that centered on cause-and-effect systems like automobile braking systems, how a bicycle pump works, or cloud formation. However, subsequent investigations found that the modality effect extended to other areas of learning.

Empirically established principles

A. The learner should have the sense that someone is talking directly to them when they hear the narration.
B. Your learner should feel like someone is talking directly to them when they hear your narration.
Also, research suggests that using a polite tone of voice ("You may want to try multiplying both sides of the equation by 10.") leads to deeper learning for low prior knowledge learners than does a less polite, more directive tone of voice ("Multiply both sides of the equation by 10."), but may impair deeper learning in high prior knowledge learners.[38][39] Finally, adding pedagogical agents (computer characters) can help if used to reinforce important content. For example, have the character narrate the lesson, point out critical features in on-screen graphics, or visually demonstrate concepts to the learner.[40][41][42][43][44]

Such principles may not apply outside of laboratory conditions. For example, Muller found that adding approximately 50% additional extraneous but interesting material did not result in any significant difference in learner performance.[55] There is ongoing debate concerning the mechanisms underlying these beneficial principles,[56] and on what boundary conditions may apply.[57]

Learning theories

Good pedagogical practice has a theory of learning at its core. However, no single best-practice e-learning standard has emerged, and may be unlikely given the range of learning and teaching styles, the potential ways technology can be implemented and the ways in which educational technology itself is changing.[58] Various pedagogical approaches or learning theories may be considered in designing and interacting with e-learning programs.

Social-constructivist – this pedagogy is particularly well afforded by the use of discussion forums, blogs, wiki and on-line collaborative activities. It is a collaborative approach that opens educational content creation to a wider group including the students themselves. The One Laptop Per Child Foundation attempted to use a constructivist approach in its project.[59]

Laurillard's conversational model[60] is also particularly relevant to eLearning, and Gilly Salmon's Five-Stage Model is a pedagogical approach to the use of discussion boards.[61]

Cognitive perspective focuses on the cognitive processes involved in learning as well as how the brain works.[62]

Emotional perspective focuses on the emotional aspects of learning, like motivation, engagement, fun, etc.[63]

Behavioural perspective focuses on the skills and behavioural outcomes of the learning process. Role-playing and application to on-the-job settings.[64]

Contextual perspective focuses on the environmental and social aspects which can stimulate learning. Interaction with other people, collaborative discovery and the importance of peer support as well as pressure.[65]

Mode neutral Convergence or promotion of ‘transmodal’ learning where online and classroom learners can coexist within one learning environment thus encouraging interconnectivity and the harnessing of collective intelligence.[66]

For many theorists it’s the interaction between student and teacher and student and student in the online environment that enhances learning (Mayes and de Freitas 2004). Pask’s theory that learning occurs through conversations about a subject which in turn helps to make knowledge explicit has an obvious application to learning within a VLE.[67]

Salmon developed a five-stage model of e-learning and e-moderating that for some time has had a major influence where online courses and online discussion forums have been used.[68] In her five-stage model individual access and the ability of students to use the technology are the first step to involvement and achievement. The second step involves students creating an identity online and finding others with whom to interact; online socialisation is a critical element of the e-learning process in this model. In step 3 students are giving and sharing information relevant to the course to each other. Collaborative interaction amongst students is central to step 4. The fifth step in Salmon’s model involves students looking for benefits from the system and using resources from outside of it to deepen their learning. Throughout all of this the tutor/teacher/lecturer fulfills the role of moderator or e-moderator, acting as a facilitator of student learning.

Some criticism is now beginning to emerge. Her model does not easily transfer to other contexts (she developed it with experience from an Open University distance learning course). It ignores the variety of learning approaches that are possible within computer mediated communication (CMC) and the range of learning theories that are available (Moule 2007).

Self-regulation

Self-regulated learning refers to several concepts that play major roles in learning, and which have significant relevance in e-learning. Zimmerman (1998) explains that in order to develop self-regulation, learning courses should offer opportunities for students to practice strategies and skills by themselves. Self-regulation is also strongly related to a student's social sources such as parents and teachers. Moreover, Steinberg (1996) found that high-achieving students usually have high-expectation parents who monitor their children closely.[69]

With the academic environment, self-regulated learners usually set their academic goals and monitor and react themselves in process in order to achieve their goals. Schunk argues, "students must regulate not only their actions but also their underlying achievement-related cognitions, beliefs, intentions and affects"(p. 359). Moreover, academic self-regulation also helps students develop confidence in their ability to perform well in e-learning courses.[69]

Theoretical framework

E-learning literature identifies an ecology of concepts, from a bibliometric study were identified the most used concepts associated with the use of computers in learning contexts, e.g. computer assisted instruction (CAI), computer assisted learning (CAL), computer-based education (CBE), e-learning, learning management systems (LMS), self-directed learning (SDL), and massive open online courses (MOOC). All these concepts have two aspects in common: learning and computers; except the SDL concept, which derives from psychology, and does not necessarily apply to computer usage. These concepts are yet to be studied in scientific research, and stand in contrast to MOOCs. Nowadays, e-learning can also mean massive distribution of content and global classes for all the Internet users. E-learning studies can be focused on three principal dimensions: users, technology, and services. According to Aparicio, Bacao & Oliveira[70] "The e-learning systems' theoretical framework contains the three main components of information systems. These components are people, technologies, and services. People interact with e-learning systems. E-learning technologies enable the direct or indirect interaction of the different groups of users. Technologies provide support to integrate content, enable communication, and provide collaboration tools. E-learning services integrate all the activities corresponding to pedagogical models and to instructional strategies. The complex interaction combination is the direct or indirect action with e-learning systems. At the same time, systems provide services according to the specified strategies for activities. In other words, service specifications are e-learning activities aligned with the e-learning pedagogical models and the instructional strategies".

Teacher use of technology

Computing technology was not created by teachers. There has been little consultation between those who promote its use in schools and those who teach with it. Decisions to purchase technology for education are very often political decisions. Most staff using these technologies did not grow up with them.[71] Training teachers to use computer technology did improve their confidence in its use, but there was considerable dissatisfaction with training content and style of delivery.[72] The communication element in particular was highlighted as the least satisfactory part of the training, by which many teachers meant the use of a VLE and discussion forums to deliver online training (Leask 2002). Technical support for online learning, lack of access to hardware, poor monitoring of teacher progress and a lack of support by online tutors were just some of the issues raised by the asynchronous online delivery of training (Davies 2004).

Newer generation web 2.0 services provide customizable, inexpensive platforms for authoring and disseminating multimedia-rich e-learning courses, and do not need specialised information technology (IT) support.[73]

Pedagogical theory may have application in encouraging and assessing on-line participation.[74] Assessment methods for on-line participation have reviewed.[74]

See also

References

  1. Mayer, R. E.; R. Moreno (1998). "A Cognitive Theory of Multimedia Learning: Implications for Design Principles" (PDF).
  2. Moreno, R. & Mayer, R. (1999). "Cognitive principles of multimedia learning: The role of modality and contiguity". Journal of Educational Psychology. 91 (2): 358–368. doi:10.1037/0022-0663.91.2.358.
  3. 1 2 Mayer, R. E. (2001). Multimedia learning. New York: Cambridge University Press. ISBN 0-521-78749-1.
  4. Mayer, R. E., & Moreno, R., "Nine ways to reduce cognitive load in multimedia learning." Educational psychologist, 38(1), 43-52, 2003.
  5. Moreno, R., & Mayer, R, "Interactive multimodal learning environments." Educational Psychology Review, 19(3), 309-326, 2007.
  6. Clark, R. C., Nguyen, F., & Sweller, J., "Efficiency in learning: Evidence-based guidelines to manage cognitive load." John Wiley & Sons, 2011.
  7. Harskamp, E. G., Mayer, R. E., & Suhre, C., "Does the modality principle for multimedia learning apply to science classrooms?" Learning and Instruction, 17(5), 465-477, 2007.
  8. Chang, C. C., & Yang, F. Y., "Exploring the cognitive loads of high-school students as they learn concepts in web-based environments," Computers & Education, 55(2), 673-680, 2010.
  9. Issa, N., Mayer, R. E., Schuller, M., Wang, E., Shapiro, M. B., & DaRosa, D. A., "Teaching for understanding in medical classrooms using multimedia design principles," Medical education, 47(4), 388-396, 2013.
  10. Mousavi, S. Y., Low, R., & Sweller, J., "Reducing cognitive load by mixing auditory and visual presentation modes," Journal of educational psychology, 87(2), 319, 1995.
  11. Gerven, P. W., Paas, F., Merriënboer, J. J., Hendriks, M., & Schmidt, H. G., "The efficiency of multimedia learning into old age," British Journal of Educational Psychology, 73(4), 489-505, 2003.
  12. Spanjers, I. A. E., Wouters, P., Van Gog, T., & Van Merriënboer, J. J. G., "An expertise reversal effect of segmentation in learning from animations," Computers in Human Behavior, 27, 46-52, 2011.
  13. Spanjers, I. A., Wouters, P., Van Gog, T., & Van Merrienboer, J. J., "An expertise reversal effect of segmentation in learning from animated worked-out examples," Computers in Human Behavior, 27(1), 46-52, 2011.
  14. Blayney, P., Kalyuga, S., & Sweller, J. "Interactions between the isolated–interactive elements effect and levels of learner expertise: Experimental evidence from an accountancy class," Instructional Science, 38(3), 277-287, 2010.
  15. Kalyuga, S., Chandler, P., & Sweller, J., "Incorporating learner experience into the design of multimedia instruction," Journal of Educational Psychology, 92, 126–136, 2000.
  16. Sweller, J (June 1988). "Cognitive load during problem solving: Effects on learning". Cognitive Science 12 (2): 257–285.
  17. Mayer, R.E., Bove, W., Bryman, A., Mars, R., & Tapangco, L., "When less is more: Meaningful learning from visual and verbal summaries of science textbook lessons." Journal of Educational Psychology, 88, 64–73, 1996.
  18. Harp, S.F., & Mayer, R.E. "How seductive details do their damage: A theory of cognitive interest in science learning." Journal of Educational Psychology, 90, 414–434, 1998.
  19. Mousavi, S. Y., Low, R., & Sweller, J., "Reducing cognitive load by mixing auditory and visual presentation modes." Journal of educational psychology, 87(2), 319, 1995.
  20. Mayer, R.E., & Moreno, R., "A split-attention effect in multimedia learning: Evidence for dual coding hypothesis." Journal of Educational Psychology, 83, 484–490, 1998.
  21. 1 2 Moreno, R., "Optimizing learning from animations by minimizing cognitive load: Cognitive and affective consequences of signaling and segmentation methods." Applied Cognitive Psychology, 21, 765–781, 2007.
  22. Spanjers, I. A., van Gog, T., Wouters, P., & van Merriënboer, J. J., "Explaining the segmentation effect in learning from animations: The role of pausing and temporal cueing." Computers & Education, 59(2), 274-280, 2012.
  23. Florax, M., & Ploetzner, R., "What contributes to the split-attention effect? Role of text segmentation, picture labeling, and spatial proximity." Learning and Instruction, 20, 216–224, 2010.
  24. Baddeley, A.D.; G.J. Hitch (1974). "Working Memory". In Bower, G.A. The psychology of learning and motivation: advances in research and theory (PDF). 8. New York: Academic Press. pp. 47–89.
  25. 1 2 3 4 5 6 7 8 9 10 Clark, Ruth C.; Mayer, Richard E. (2011). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. (3rd ed.). John Wiley & Sons.
  26. "Basic principles for online and multimedia learning". www.edgurus.com. EdGurus.com. Retrieved 13 March 2015.
  27. Park, B., Moreno, R., Seufert, T., & Brünken, R., "Does cognitive load moderate the seductive details effect? A multimedia study." Computers in Human Behavior, 27(1), 5-10, 2011.
  28. Magner, U. I., Schwonke, R., Aleven, V., Popescu, O., & Renkl, A., "Triggering situational interest by decorative illustrations both fosters and hinders learning in computer-based learning environments." Learning and Instruction, 29, 141-152, 2014.
  29. Crooks, S. M., Cheon, J., Inan, F., Ari, F., & Flores, R. "Modality and cueing in multimedia learning: Examining cognitive and perceptual explanations for the modality effect." Computers in Human Behavior, 28(3), 1063-1071, 2012.
  30. Heimbuch, S., & Bodemer, D. (2014). Supporting Awareness of Content-related Controversies in a Wiki-based Learning Environment. In Proceedings of the 10th International Symposium on Open Collaboration (OpenSym 2014), 30:1–4. New York, USA: ACM.
  31. Ibrahim, M., Antonenko, P. D., Greenwood, C. M., & Wheeler, D. "Effects of segmenting, signalling, and weeding on learning from educational video." Learning, Media and Technology, 37(3), 220-235, 2012.
  32. Hasler, B. S., Kersten, B., & Sweller, J., "Learner control, cognitive load and instructional animation." Applied cognitive psychology, 21(6), 713-729, 2007.
  33. 1 2 Savoji, A.P.; Hassanabadi, H.; Fasihipour, Z. (2011). "The modality effect in learner-paced multimedia learning.". Procedia-Social and Behavioral Sciences. 30: 1488–1493.
  34. Khacharem, A., Spanjers, I. A., Zoudji, B., Kalyuga, S., & Ripoll, H., "Using segmentation to support the learning from animated soccer scenes: An effect of prior knowledge." Psychology of Sport and Exercise, 14(2), 154-160, 2013.
  35. Spanjers, I. A., Wouters, P., Van Gog, T., & Van Merrienboer, J. J., "An expertise reversal effect of segmentation in learning from animated worked-out examples." Computers in Human Behavior, 27(1), 46-52, 2011.
  36. Hatsidimitris, G., & Kalyuga, S., "Guided self-management of transient information in animations through pacing and sequencing strategies." Educational Technology Research and Development, 61(1), 91-105, 2013
  37. Kartal, G., "Does language matter in multimedia learning? Personalization principle revisited." Journal of Educational Psychology, 102(3), 615, 2010.
  38. Wang, N., Johnson, W. L., Mayer, R. E., Rizzo, P., Shaw, E., & Collins, H., "The politeness effect: Pedagogical agents and learning outcomes." International Journal of Human-Computer Studies, 66(2), 98-112, 2008.
  39. McLaren, B.M., DeLeeuw, K.E., & Mayer, R.E. (2011). A politeness effect in learning with web-based intelligent tutors. International Journal of Human Computer Studies, 69, 70–79, 2011.
  40. Mayer, R.E., Dow, G., & Mayer, S., "Multimedia learning in an interactive self-explaining environment: What works in the design of agent-based microworlds?" Journal of Educational Psychology, 95, 806–813, 2003.
  41. Moreno, R., Mayer, R.E., Spires, H., & Lester, J., "The case for social agency in computer-based teaching: Do students learn more deeply when they interact with animated pedagogical agents?" Cognition and Instruction, 19, 177–214, 2001.
  42. Atkinson, R.K. (2002). Optimizing learning from examples using animated pedagogical agents. Journal of Educational Psychology, 94, 416–427, 2002.
  43. Mayer, R. E., & DaPra, C. S., "An embodiment effect in computer-based learning with animated pedagogical agents." Journal of Experimental Psychology: Applied, 18(3), 239, 2012.
  44. Moreno, R., Reislein, M., & Ozogul, G., "Using virtual peers to guide visual attention during learning." Journal of Media Psychology: Theories, Methods, and Applications, 22(2), 52-60, 2010.
  45. Mayer, R.E., Mathias, A., & Wetzell, K., "Fostering understanding of multimedia messages through pretraining: Evidence for a two-stage theory of mental model construction." Journal of Experimental Psychology: Applied, 8, 147–154, 2002.
  46. Pollock, E., Chandler, P., & Sweller, J., "Assimilating complex information." Learning and Instruction, 12, 61–86, 2002.
  47. Ayres, P., "Impact of reducing intrinsic cognitive load on learning in a mathematical domain." Applied Cognitive Psychology, 20(3), 287-298, 2006.
  48. Clarke, T., Ayres, P., & Sweller, J., "The impact of sequencing and prior knowledge on learning mathematics through spreadsheet applications." Educational Technology Research and Development, 53(3), 15-24, 2005.
  49. Moreno, R., & Mayer, R.E., "Verbal redundancy in multimedia learning: When reading helps listening." Journal of Educational Psychology, 94, 156–163, 2002.
  50. Scheiter, K., Schüler, A., Gerjets, P., Huk, T., & Hesse, F. W., "Extending multimedia research: How do prerequisite knowledge and reading comprehension affect learning from text and pictures." Computers in Human Behavior, 31, 73-84, 2014.
  51. Chandler, P., & Sweller, J., "Cognitive load theory and the format of instruction." Cognition and Instruction, 8, 293-332, 1991.
  52. Kalyuga, S., Chandler, P., & Sweller, J. "Incorporating learner experience into the design of multimedia instruction." Journal of Educational Psychology, 92, 126–136, 2000.
  53. McLaren, B.M., DeLeeuw, K.E., & Mayer, R.E. "A politeness effect in learning with web-based intelligent tutors." International Journal of Human Computer Studies, 69, 70–79, 2011.
  54. Magner, U. I., Schwonke, R., Aleven, V., Popescu, O., & Renkl, A. "Triggering situational interest by decorative illustrations both fosters and hinders learning in computer-based learning environments." Learning and Instruction, 29, 141-152, 2014.
  55. Muller, D. A.; Lee, K. J.; Sharma, M. D. (2008). "Coherence or interest: Which is most important in online multimedia learning?" (PDF). Australasian Journal of Educational Technology. 24 (2): 211–221. Retrieved October 19, 2008.
  56. Tabbers, Martens, Merriënboer. "The modality effect in multimedia instructions" (PDF). Open University of the Netherlands. Retrieved 2012-01-25.
  57. Reinwein (2012). "Does the Modality Effect Exist? and if So, Which Modality Effect?". Journal of Psycholinguistic Research.
  58. Meredith, S. and B. Newton (2003). "Models of eLearning: Technology Promise vs Learner Needs Literature Review." The International Journal of Management Education 3(3).
  59. Wiki.Laptop.org
  60. Informal description of Laurillard's Model
  61. E-moderating: The Key to Teaching and Learning Online – Gilly Salmon , Kogan Page, 2000, ISBN 0-7494-4085-6
  62. Bloom, B. S., and D. R. Krathwohl. (1956). Taxonomy of Educational Objectives: Handbook 1
  63. Bååth, J. A. (1982) "Distance Students' Learning – Empirical Findings and Theoretical Deliberations"
  64. Areskog, N-H. (1995) The Tutorial Process – the Roles of Student Teacher and Tutor in a Long Term Perspective
  65. Black, J. & McClintock, R. (1995) "An Interpretation Construction Approach to Constructivist Design."
  66. Smith B, Reed P & Jones C (2008) ‘Mode Neutral’ pedagogy. European Journal of Open, Distance and E-learning."
  67. Allen, I. E., J. Seaman, et al. (2007). Blending In: The Extent and Promise of Blended Education in the United States. Needham, M.A., The Sloan Consortium.
  68. Salmon, G. (2005). E-moderating, the key to teaching and learning online. Routledge Falmer.
  69. 1 2 Peter E. Williams and Chan M. Hllman(Feb.,2004). Differences in self-regulation for online learning between first-and second-generation college students.Research in Higher Education, Vol. 45, No.1, pp. 71-82.http://www.jstor.org/stable/40197287
  70. Aparicio, M.; Bacao, F.; Oliveira, T. (2016). "An e-Learning Theoretical Framework" (PDF). Educational Technology & Society. IEEE. 19(1): 292–307.
  71. Laurillard, D. (2006). Rethinking University Teaching: a framework for the effective use of learning technologies. Abingdon, Oxon., RoutledgeFalmer.
  72. Galanouli, D., C. Murphy, et al. (2004). "Teachers' perceptions of the effectiveness of ICT-competence training." Computers and Education 43(1-2): 63-79.
  73. Tam CW, Eastwood A. Available, intuitive and free! Building e-learning modules using web 2.0 services.Med Teach. 2012;34(12):1078-80. doi:10.3109/0142159X.2012.731105
  74. 1 2 Ho, S. (2002). "Evaluating students' participation in on-line discussions".
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