Personalization Principle

Overview

Identified as an advanced principle of the Cognitive Theory of Multimedia Learning, the Personalization Principle hypothesizes that having narrated and textual personalized messages integrated into learning material could likely lead to more meaningful learning than messages that are not personalized (Moreno & Mayer, 2000, 2004). Furthermore, it is theorized that the addition of personalized messages that are relevant to the learning material, and that are thoughtfully designed to exclude extraneous elements (ex. seductive details) minimizing cognitive load, will foster greater generative processing of the knowledge to be learned in the learner. It is thought that personalization helps the learner perceptually place themselves into the learning task as a participant. Through the affective and motivational processes of relatedness and perceptions of ownership of learning, they could become more invested in the learning tasks and material leading to more active processing of the new information. CATLM (Moreno, 2005; Moreno & Mayer , 2007).

Evidence

In a series of experiments conducted by Mareno and Mayer in 2000, and 2004, two groups were tasked to play the educational botany game Design-a-Plant (Lester, Stone, & Stelling, 1999), read or listen to an explanation on lightning formation, and listen to a narration about the lungs. In each study, participants were split into the groups of personalized (experimental) and non-personalized (control) of the multimedia content. The results showed that in all ten experiments, the experimental group with personalized messages outperformed the control group with no personalized messages on a transfer test, altogether averaging a median score of 1.30 (significantly large effect).

Design Implications

As mentioned in the overview, for generative processing to be effectively increased through the integration of personalized messages in multimedia learning content, the messages must be relevant to the actual content being communicated and attention must be made by the designers to minimize extraneous content, to make room for essential and generative processing of the learning material as extraneous and essential processing are additive. A good example of designers intent on aiding generative processing via personalization is present in Sal Khan’s Introduction to Economics video lecture (Khan Academy, 2012). At the timestamp 5:26, Khan endocrines the audience into the shoes of a micro/macro economic theorist; one that is trying to decide on how to go about the calculation of the decisions of people and the whole of society. Through the usage of personalized phrases such as, “And so, you can start to visualize things mathematically, with charts and graphs and think about what would actually happen with markets” (fig 1) and “So then you have a proper grain of salt so that you are always focused on the true intuition.” Khan is going a step further than simply using personalized pronouns (ex. “you” and “we”), as he is incorporating the audience into the thought process of the macro/micro theorist and in the process promoting active thinking of the learner.

Figure 1

An increase in generative processing can be hindered when personalization is used just for personalization sake. This is found to be very prominent in The Learning Companies edutainment game Reader Rabbit Math Ages 6-9. Although the pedagogical agent Penelope the Parrot addresses the player personally, even going far enough to make eye contact with the player, the game suffers from only about 1/3rd of these personalized interactions being used to communicate the learning content. Instead the majority of these messages are used to communicate how each mechanism works in the math minigames, and to state that a solution is correct or incorrect. Hardly does Penelope give any indication on why a solution is correct and incorrect. Nor does she provide much feedback on how to improve a player's answers (i.e. pointing out gaps in the players' knowledge and offering different strategies to help players better understand the material). This can be seen in the Monkey Pizza Party minigame where the objective is to add different bug toppings on a pizza according to a fractions given to the player each round. incorrectly distributing the toppings on the pizza (9:46), leads Penelope to inform the player that the solution is incorrect and to look at the order (equation) again.

Figure 2

Another ill-structured use of personalization for generative processing in this game can be found in the mining Number line minigame (6:42). Although the learning outcome of this minigame is to gain the knowledge and skills to count up to 2 number places with the assistance of a number line, the personalized messages from Penelope are only used for accessibility (auditory reciting's of the prompt on screen). There is no usage of personalized messages in this minigame to help increase understanding of the learning content. This seems like a missed opportunity on the designers' part.

Figure 3

Challenges

Although research on personalized messages in a variety of interactive and non-interactive multimedia settings has shown significant improvements in transfer scores than multimedia environments with formal messages (Moreno & Mayer, 2000, 2004), sensitive procedures in measuring subjects cognitive load has been lacking. One of the Design-A-Plant experiments by Moreno & Mayer (2004) did find that participants of the personalized message group had reported significantly lower levels of perceived cognitive load, than students of the non personalized group (d=0.67), however there could have been other underlying factors that had brought about these results. In any case, more research should be carried out that involves sensitive procedures for measuring generative, extraneous, and essential processing, in the aim to provide more evidence that personalized messages lead to significantly less cognitive processing than formal messages in multimedia environments.

References

khan, S. (2012, June 28). Introduction to economics | supply, demand, and market equilibrium | economics | khan academy. YouTube. Retrieved December 16, 2022, from https://www.youtube.com/watch?v=8JYP_wU1JTU

Lester, J. C., Stone, B. A., & Stelling, J. D. (1999). Lifelike pedagogical agents for mixed-initiative problem solving in constructivist learning environments. User Modeling and User-Adapted Interaction, 9, 1–44.

letsplayer247. (2018, March 18). Reader rabbit math ages 6-9 gameplay. YouTube. Retrieved December 16, 2022, from https://www.youtube.com/watch?v=838EoFa5gSs&t=496s

Moreno, R. (2005). Instructional technology: Promise and pitfalls. In L. PytlikZillig, M. Bodvarsson, & R. Bruning (Eds.), Technology-based education: Bringing researchers and practitioners together(pp.1–19). Greenwich, CT: Information Age Publishing.

Moreno, R.,& Mayer, R.E.(2000). Engaging students in active learning: The case for personalized multimedia messages. Journal of Educational Psychology, 92, 724–733.

Moreno, R.,& Mayer, R.E.(2004). Personalized messages that promote science learning in virtual environments. Journal of Educational Psychology, 96, 165–173.

Moreno, R., & Mayer, R.E.(2007). Interactive multimodal learning environments. Educational Psychology Review, 19, 309–326.

Moreno, R., & Mayer, R. E. (2010). Techniques that increase generative processing in multimedia learning: Open questions for cognitive load research. In J. L. Plass, R. Moreno, & R. Brunken (Eds.), Cognitive Load Theory (pp. 153–178). Cambridge: Cambridge University Press.

Reader Rabbit (1998). Reader Rabbit Math Adventure Ages 6-9. The Learning Company.