Raymond S. Pastore, Ph.D.
What is multimedia? Verbal and Non Verbal representations better for
learning than just one (Mayer, 2005) Modality effect
Audio and visual representations are recommended for instructional delivery (Low & Sweller, 2005) This reduces the cognitive load placed on the
learner by allowing them to focus on the image and listen to the narration at the same time.
Split attention principle - When learners split their attention between text and visuals, they are increasing cognitive load because they are trying to integrate two sources of information that are required for comprehension.
But we care about…. Rapid Fastest Cheapest (cost effective) Time Savings Training?
Research Problem
Research Problem Recent research has revealed that audio can be
compressed up to a certain point and still support high level learning (Pastore, 2009)
What is Time Compressed Speech? (Orr & Friedman, 1967) The act of changing the tempo while preserving the pitch and retaining quality Invariant timing hypothesis (Honing, 2007).
This hypothesis states that one cannot distinguish between the quality of audio when there is a tempo change (within normal wpm ranges and up to a certain point of compression).
Pastore (2009) revealed that spoken words could be digitally time-compressed up to 25% in a multimedia environment while preserving complex learning.
Thus, a one-hour multimedia session could take 45 minutes if time-compressed 25% and still maintain the same level of learner comprehension.
Research Problem Redundancy Principle
Would time-compressed speech be more effective if text was provided? This goes against redundancy principle
Current research has revealed a possible reversal effect associated with time-compressed instruction on factual knowledge (Ritzhaupt, Gomes, & Barron, 2008). The reversal effect suggests that groups who
received redundant representations outperformed groups who received non-redundant representations.
At this time however, no research has examined time-compressed instruction and redundancy on high-level learning objectives.
The purpose of this study was to examine 1) the effects of time-compressed
instruction on learning complex material in a multimedia environment
2) the redundancy principle on complex material in a multimedia environment and
3) the impacts of cognitive load on both time-compression and redundancy in a multimedia environment.
Methodology Participants - 154 University Students (61
males/93 females)
Instruction 2,000-word script - based on the physiology and
function of the human heart and its parts (Dwyer, 1965) 19 Simple line drawings Audio Narration
Audio Compression Audacity WSOLA
Design - 2x3 Experimental Design Independent Variables -
Redundant and Non-redundant Compressed Instruction - 0%, 25%, 50%
0% Compression 25% Compression 50% Compression n
Redundant 25 27 25 77
Non-Redundant
25 25 27 77
n 50 52 52 154
- Demographics Survey- Pre-Test- Dependent Variables:
Post Tests - Identification - measures students’ understanding of
factual knowledge Comprehension - measures students’ understanding of
problem solving Cognitive Load – direct subjective measure given
immediately after treatment Time-on-Task Review Behaviors - Back and Replay Buttons
Methodology – Sample without text
Methodology –redundant text
Methodology - Treatments
Compression Percentage - Time
Time Words Per Minute
0% 12:10 164
25% 9:08 219
50% 6:05 328
Methodology - Procedure University Computer lab – Mac lab Each student had their own
headphones/computer station. They were given a CD with treatment
and directions. Study took 45-60 minutes to complete Introduction to study, demographics
survey, pre-test, treatment, DVs
Findings Prior knowledge (2x3 ANOVA) - No differences Posttest Measures (2x3 MANOVA)
Redundancy Non-Redundant (11.78) > Redundant (9.17) – factual Non-Redundant (8.34) > Redundant (7.04) – Problem solving *numbers indicate means; out of 20 points.
Compression (Tukey’s HSD for post hoc) 0% (11.82) = 25% (11.02) > 50% (8.63) - factual 0% (8.84) = 25% (8.25) > 50% (6.02) - Problem solving *numbers indicate means; out of 20 points.
Cognitive Load (2x3 ANOVA) Redundant (3.83) = Non-redundant (3.48) 0% (3.09) = 25% (3.36) > 50% (4.68) *numbers indicate means; out of 7 points; 7 highest load.
Time On Task (2x3 ANOVA): 0% < 25% < 50% Review Behaviors Load (2x3 ANOVA): No significant differences
among groups
Conclusions and Recommendations
The results of both the low (factual) and high (problem solving) level learning tasks in this study revealed that multimedia instruction can be compressed up to 25% without sacrificing learning.
Computer based training should use compressed narration (where appropriate) to take advantage of the speed associated with reading as well as the modality effect
This could translate to significant financial savings when a one-hour computer based training session is reduced to only 45 minutes, which would accumulate more time saved by each employee who completes it
Conclusions and Recommendations Implementation would be most significant
during large-scale training efforts where hundreds or thousands of employees are training at one time.
Essentially there is a large return on investment when choosing to use this technology
Compressing audio takes very little time and effort, thus, it should not significantly impact development time and should not increase development costs as compression can be done using free software
Future Research How it applies to others types of media
(i.e., mobile) Prior Knowledge Preferences Redundancy – limited text
Follow up study What speeds do students prefer? Students given samples of multimedia
and just audio and asked the following: Would you prefer this for learning?
Lectures? Books? Entertainment? Etc. and asked to rate on 7 point scale.
Students taught how to do time compression and surveyed.
Questions? Ray Pastore: [email protected] Website and blog: raypastore.com Presentation:
http://raypastore.com/wordpress/2011/11/aect-time-compression-presentation/
Paper citation: Pastore, R. (2012). The effects of time-
compressed instruction and redundancy on learning and learners’ perceptions of cognitive load. Computers & Education, 58(1), 641-651.