This research paper was written in 2013 as part of my Ph.D. studies in the educational technology program at the University of Tartu. I submitted it to Music Education Research, but it was not accepted. I did not resubmit it, and now, ten years later, I am publishing it here.
This article is very interesting; the research is well constructed and examines an area that is very important to current music education. I believe with a few minor revisions the article should be published in MER.Reviewer 1
Although the study design seems adequate, I am not convinced that this article is strong enough to be published in Music Education Research. I suggest that the author re-work the article, address the language issues, and submit this to a different journal.Reviewer 2
Learning Guitar with the Help of Multimedia: Does the Physical Environment Matter?
Background audio and video have been found to be distracting while learning. Since much of the multimedia learning among hobby musicians takes place in home environments, we could enquire how the potentially different surroundings relate to the musical results. This paper investigates the audio-visual characteristics of the authentic physical environments of 129 adults when studying guitar using multimedia material. The correlational survey was conducted to confirm whether background audio has statistically significant correlations with the musical outcome for experienced learners and not in the case of novices. This is explained in terms of the limitations of working memory. Both novices and experienced learners reported having studied in comparatively quiet surroundings, and their estimations about the physical environment did not differ significantly from each other. The characteristics of learning environments are presented, and potential sources of extraneous background audio and video are proposed.
Keywords: multimedia learning; musical instrument; guitar; authentic learning environments
Technological progress in the current decade has allowed people to access music education more easily than ever (Partti, Karlsen 2010). Complicated and expensive solutions, using a dedicated optical fiber network, special classrooms, TV screens, cameras, and microphones as described by Rees and Downs (1995), frequently are being replaced by a single wireless device allowing the music student both synchronous and asynchronous communication with the teacher and the multimedia learning material even from the most distant parts of the world (Brändström, Wiklund, Lundström 2012). In addition to technological advances, arguments that claim musicality is an acquired skill rather than something present at birth make it understandable that people take up music later in life (Marcus 2012). These trends allow music education to exist outside institutions, resulting in a rich variety of real-life learning contexts that may or may not relate to the musical outcome.
This paper focuses on the auditory and visual characteristics of the physical learning environments of individual volunteers who have studied the basics of guitar playing with the help of multimedia study material. The term ‘multimedia learning’ in this article is used as defined by Mayer and Moreno (2010).
Although research addressing music instrument tuition in the context of modern multimedia learning theories is still rare, this cannot be said of multimedia learning in general, and many findings and principles of this work may be applicable to the music education context.
Cognitive theory of multimedia learning
The cognitive theory of multimedia learning (CTML) assumes that learners have separate channels for processing auditory and visual information; the channels have limited capacity, and that learning includes selecting and organizing relevant information and integrating it with existing knowledge (Mayer, Moreno 1998, 2010; Moreno, Mayer 2000). CTML is a more specialized theory than cognitive load theory (CLT) (Chandler, Sweller 1991; Sweller 1994, 2010), but it is consistent with its primary goal to design instructional materials that help prevent the learner from cognitive overload (Mayer, Moreno 2010).
CTML distinguishes between three categories of cognitive processing: (1) extraneous processing caused by poor instructional design and irrelevant information (corresponds to extraneous cognitive load in CLT), (2) essential processing caused by the actual, intrinsic difficulty of the material (intrinsic cognitive load in CLT), and (3) generative processing that helps to integrate and organize the material (germane cognitive load in CLT). The first is to be reduced, the second to be managed, and the third to be fostered (Mayer, Moreno 2010).
When designing multimedia instruction to teach a musical instrument, three sources of information should be considered: (1) instructional material, (2) the learner’s own playing, and (3) the physical surroundings. The first two sources contain information that is particularly relevant to the learning process. At the same time, the physical background does not contain information that is essential to the learning process, but as the ‘intrinsic and extraneous cognitive load are additive’ (Sweller 2010, 40), it still competes for the learner’s memory resources. Therefore, investigating the physical learning environment and the possible relationships between its characteristics and the musical result may provide valuable knowledge for designing more efficient multimedia materials for learning how to play a musical instrument.
Multimedia learning and music
When Partti and Karlsen (2010) describe the ways an average teenager can become engaged with music, they mention mobile phones, video games, different computer programs, and portable audio players, which are all capable of presenting multimedia content. In March 2010, iPad was released, and its ever-growing repository of music-related applications has found its way into music curricula (Miller 2012). While using multimedia to teach and learn to play musical instruments is hardly a novelty, the body of relevant research literature – that would connect modern multimedia learning theories and music instrument instruction – has yet to emerge.
For example, Truman and Truman (2006) used a music-oriented e-learning system as study material to investigate the situated learnability effects of single- and dual-modal systems. Undergraduate students (n=40) were assigned four conditions: music-oriented learning environment with text+narration and text only, and science lessons with text+narration and text only. Although published in 2006, this paper does not mention CLT or CTML. Nevertheless, the findings are consistent with those theories, as the authors found that the text+narration combination increased learnability compared to text only.
Yu et al. (2010) developed a multimedia learning system that was proven to help primary school pupils (mean age 9.6, n=64) learn musical notation. The children received 12 x 40 min multimedia lessons, and the experimental group’s (n=32) material was enhanced by the use of graphics. Aldalalah and Fong (2010a) also designed a music theory lesson for third-grade students (n=405) to investigate the modality and redundancy principles as described by CTML (Moreno, Mayer 2010) in the case of different levels of musical intelligence. The test for measuring musical intelligence was developed by the authors. The audio+image combination was found to be superior to text+image and audio+text+image combinations across the different levels of musical intelligence. The authors of both studies found the results consistent with the principles of CTML.
However, in these three experiments, music theory was taught rather than practical music. By applying the knowledge that comes from these and many other multimedia learning studies, it is possible to develop instructional materials that use human resources efficiently – at least under controlled circumstances, because one of the limitations of current multimedia research is that it is usually based on short-term instruction given during laboratory experiments (Mayer, Moreno 2010). This is, of course, reasonable, as the experimental designs allow the researcher to ensure that most of the conditions are identical, but what happens if the conditions become unbalanced, varied, and individual, as in many real-life situations? What happens if the learner leaves the classroom? In their annual report ‘Europe Digital Future in Focus 2013’, the internet monitors comScore.com state that 20% of web page views in Europe came from mobile phones and tablet computers (32% in the UK) (as of May 15, 2013, from www.comscore.com/Insights/Blog/2013_Digital_Future_in_Focus_Series). The increasing availability and popularity of highly portable devices, along with the widespread accessibility of wireless internet, allow people to consume multimedia products almost everywhere. This potentially results in a rich variety of learning environments that seem to be out of the instructor’s control.
Multimedia learning and physical environments
Researchers have investigated the role and the effects of the auditory and visual background while learning and found that irrelevant sounds – both within multimedia learning material (e.g., Fassbender et al. 2012; Moreno, Mayer 2000) and outside the material (e.g., Dobbs, Furnham, McClelland 2011; Furnham, Strbac 2010) – have largely been found to be detrimental to the resulting performance. Consuming different media sources simultaneously is an increasing trend (Gardner 2008), and it is no longer a question of whether the TV and other sources of visual information affect learning, but rather researchers are asking which content does what? (Lin, Lee, Robertson 2011).
Sung and Mayer (2013) used a multimedia lesson explaining the work of solar cells and found that learning from a tablet computer increased the students’ (n=89, mean age 18.6 years) motivation to continue studying compared to those who learned using desktop computers. They attributed the difference to the instructional medium. However, the two groups studied in different environments: the desktop computers were placed in separate cubicles, while the students with mobile devices studied ‘in informal locations outside the lab such as on a bench in a courtyard’ (Sung & Mayer 2013, 643). It is at least possible that the different physical environments helped to raise the students’ motivation (Papageorgi et al. 2010).
The principles of the emerging context-aware u-learning (Ubiquitous learning), where ‘students can access digital materials or feedback through mobile devices in real situations’ (Tsai, Tsai, Hwang 2011, 251), allow multimedia instruction to be adjusted to the actual learning context (Peng, Chou, Chang 2008). In a recent experiment, Liu et al. (2012) could not confirm the split-attention effect known from the CLT (Sweller 2010) in a mobile learning situation where information was coming from both virtual and physical environments. One explanation proposed by the authors is that ‘the magnitude of the positive effects created by the authentic learning experience with the real objects exceeded the magnitude of the negative split-attention effect’ (Liu et al. 2012, 178). This means that while many of the cognitive effects are well documented and usable in instructional media, the extraneous sources of information provided by different physical environments also need to be considered. Mayer and Moreno (2010) emphasize the need to take multimedia learning research to authentic, real-life learning situations and to include more learning subjects than the most extensively explored mathematics and selected science subjects.
The aim of this study has been to investigate how different real-life learning environments relate to the musical outcome of voluntary multimedia learners. The primary research question is: How does the accuracy of notes, rhythm, and tempo relate to the auditory and visual background of the physical learning environment as perceived by novice and experienced learners?
To answer the research question, a correlational survey was conducted consisting of an online questionnaire and recorded audio samples from individual learners.
The instructional material used in this paper was the first level of the guitar course – ‘Accompaniment on the Guitar’ – developed by the author and delivered through the online guitar school http://www.kitarrikool.ee (English version published as the book ‘Guitar school – The key to practical guitar playing’ [author 2008]). The course was designed for adult beginners and consists of eight levels, where information is presented via text, video, audio, graphics, tablature, and standard notation. Between 2007 and 2012, over 11,700 volunteers took this course, uploading a total of 2,502 audio recordings.
In order to participate, respondents needed to have a computer with a sound card, microphone and speakers, and a guitar. To record the audio, the use of the open-source audio recording and editing software Audacity (http://audacity.sourceforge.net) was recommended. A video tutorial instructing respondents on how to make recordings was provided. A Java applet was also designed and embedded in the course to provide an easier recording option for those unfamiliar with audio editing.
The goal of the first instruction was to teach the basic principles of the guitarist’s right and left-hand actions, the positions and names of the notes on the solo strings in the first position, to acquaint the student with the principles of notation, and eventually to master and record four exercises. The exercises were: (1) a warm-up exercise on open strings (taught by video), (2) a diatonic scale from g to g’ on the solo strings (taught by graphics, video, and notation), (3) a simple folk tune (taught by notation and audio example) and (4) a free choice from the popular melodies Brother John, Happy Birthday and an Estonian folk tune Aiaäärne tänavas (all taught by audio example only). The audio recordings were uploaded as separate files through the upload plug-in on the web page or by using the dedicated Java applet.
The first level of the course was freely available to everybody, and participation was voluntary.
All 238 Estonian-speaking learners, who had recorded and uploaded the required audio files before October 2012, were drawn from the main database (total of 11707 users) and were invited to complete the survey. The questionnaire was completed by 142 learners, but data from only 129 individuals was included in the final sample, as the audio recordings in 13 cases were of unacceptable quality or incomplete. The mean age of the final sample was 28.6 years (SD=9.93) at the time of the lessons (based on n=128, one person refused to reveal her age) – 85 participants stated that this course was their first experience learning to play the guitar while 44 people claimed to have some guitar playing experience already. In this paper, the former group will be called novices, and the latter experienced learners.
Table 1. Participants
|Under 20||28 (21.9%)|
|40 or above||18 (14.1%)|
|Played guitar before||44 (34.1%)|
An online questionnaire was designed to ask the participants how often they perceived auditory and/or visual background information while studying. A five-point Likert scale was used to measure the frequency of the auditory and visual stimuli irrelevant to the study process. To measure the auditory background, the respondents were asked, ‘How often did you learn in silence (excluding the sounds coming from the study material and your own guitar playing?’. Answers were given on the following scale: (1) Never, (2) Less than half of the times, (3) Approximately half of the times, (4) More than half of the times, and (5) Always.
To measure the frequency of extraneous visual stimuli, the respondents were asked, ‘How often did you have the TV or other screens on while learning?’. TV is one of the most distracting sources of visual background information and is often included in media multitasking research (Gardner 2008; Lin et al. 2011). Additional questions about the characteristics of the physical learning environment were asked as well as the number of learning sessions needed to complete the task. The questionnaire was designed in collaboration with researchers at the faculty of social and educational sciences and piloted before release.
Secondly, the musical result of the participants was measured. Two experts (guitar pedagogy lecturers from the university) assessed three audio recordings by each participant. The first, a warm-up exercise, was not assessed because it only consisted of three notes and lacked rhythmic variety. The assessment was divided into three categories – (1) accuracy of notes, (2) accuracy of rhythm, (3) accuracy of tempo – and all were measured on a five-point scale where ‘5’ meant that no mistakes were found. The correlation coefficients between the scores given by the two experts were as follows: accuracy of rhythm r=.871 (p=.01), the accuracy of notes r=.828 (p=.01), and the accuracy of tempo r=.664 (p=.01).
Descriptions of the characteristics of the auditory and visual background as perceived by the learners were collected, and Spearman’s correlation coefficients between the auditory and visual background characteristics and the musical score were computed using SPSS Statistics 17.0. The sample was divided into two parts, allowing us to compare data from novice and experienced learners. An independent-sample t-test was conducted to compare the mean scores, and a Fisher r-to-z transformation was used to compare the correlation coefficients.
The collected data showed that the auditory characteristics of the physical learning environment related to the musical results in different ways for novice and experienced learners, and this difference was statistically significant. The quieter the learning environment, the better the scores for rhythm and for notes among experienced learners. Novices showed almost no correlation between the audio-visual background and the quality of the musical outcome.
Overview of the descriptions
Differences between descriptions by novice and experienced learners – speed of completion and mean musical scores.
Novices said that it took more than 7 sessions (M=7.30 SD=4.51) to complete the task. Experienced learners reported having completed the task in less than 5 sessions (M=4.77 SD=3.14). The independent-sample t-test showed a statistically significant difference in the mean scores (t=-3.595, df=117, p=0.001). In addition, experienced players received higher scores for notes, rhythm, and tempo, the differences being statistically significant in the case of scores that describe timing (rhythm and tempo) but not for scores that describe the number of correct notes played by the learners (Table 2).
Table 2. Mean scores of musical results
Similarities between novice and experienced players – auditory background
Both the novice and experienced learners claimed to have studied in a rather similar environment, the mean scores on a five-point Likert scale being 4.14 (SD=.95) and 4.36 (SD=.81), respectively. The scores fall between the statements ‘More than half of the times’ and ‘Always’ on the proposed scale, and the difference is statistically insignificant (p=.189). A further question about background noise was asked to find out how often the potentially irrelevant sounds were actually perceived as disturbing. The results, 4.33 (SD=.71) for novice learners and 4.09 (SD=.12) for experienced learners, did not differ significantly either (p=.087). The majority of the learners (128 people, 99.2%) said their learning environment was their home and one (0.8%) studied at work.
Table 3. Q: How often did you learn in silence (excluding the sounds coming from the study material and your own guitar playing)?
|Never||0 (0%)||0 (0%)|
|Less than half of the times||8 (9.4%)||1 (2.3%)|
|Approx. half of the times||9 (10.6%)||6 (13.6%)|
|More than half of the times||31 (36.5%)||13 (29.5%)|
|Always||37 (43.5%)||24 (54.5%)|
The Spearman’s correlation between the reported frequency of irrelevant background sounds and the frequency of the sounds that were found to be disturbing was .342 (p=.001) for the novices, whereas the same coefficient was .194 (p=.207) for the experienced learners. The Fisher r-to-z transformation showed the difference in these correlations to be statistically insignificant (p=.4).
Table 4. Q: How often did sounds (other than those from your study material and your own guitar playing) disturb you while studying?
|Never||37 (43.5)||13 (29.5)|
|Less than half of the times||42 (49.4%)||25 (56.8%)|
|Approx. half of the times||3 (3.5%)||3 (6.8%)|
|More than half of the times||3 (3.5%)||3 (6.8%)|
|Always||0 (0%)||0 (0%)|
Similarities between novice and experienced players – visual background
Both novice and experienced learners reported having quite a similar frequency of background screens being open while studying: 4.43 (SD=.9) for novices (n=84, one answer missing) and 4.47 (SD=.83) for experienced learners (n=43, one answer missing). On the 5-point Likert scale, this result falls between the statements ‘Less than half of the times’ and ‘Never’. The difference between the mean frequencies was statistically insignificant (p=.82).
Table 5. Q: How often did you have TV or other screens (other than your computer) on while studying?
|Never||53 (62.4)||27 (61.4)|
|Less than half of the times||20 (23.5%)||11 (25%)|
|Approx. half of the times||5 (5.9%)||3 (6.8%)|
|More than half of the times||6 (7.1%)||2 (4.5%)|
|Always||0 (0%)||0 (0%)|
Relationship between perceived audio-visual background and musical results
The computed Spearman’s correlation coefficient reveals a moderate but statistically significant relationship between the experienced learners’ mean musical score and the frequency of the perceived background sounds. In the case of novices, such a relationship was not found. The correlation between visual background and mean musical score was insignificant across the sample (Table 6).
Table 6. Spearman’s correlation coefficients between mean musical score and auditory and visual background
|Mean musical score||Novices||.020||-.043|
**. Correlation is significant at the 0.01 level (2-tailed)
*. Correlation is significant at the 0.05 level (2-tailed)
Leaving the musical result aside, both novices and experienced learners had significant correlations between the frequencies of perceived background audio and screens playing while learning.
The participants reported having studied in comparatively quiet surroundings, as 80% of the novices said their surroundings were silent ‘More than half of the times’ or ‘Always’. In the case of experienced learners, the number was 84%. TV or other background screens were reported to be switched on ‘Less than half of the times’ or ‘Never’ in 85.9% of cases (novices) or 86.4% (experienced learners). Based on the comparison of the participants’ reports about the audio-visual characteristics of their learning environments, one can conclude that both novice and experienced learners perceived the auditory background rather similarly.
It was predicted that the novices would have stronger correlations between their audio background and musical results than the experienced learners. The results revealed the opposite. What could explain such a result?
One of the differences between novice and experienced players is that the latter use fewer brain resources to complete the task. The resource is either the cortical activity related to motor planning, as reported by Wright et al. (2012) after experiments with experienced and novice guitarists, or working memory, as shown by Gray (2004), with professional and amateur baseball players. This means that when executing a mastered motor skill, experienced players have more free working memory resources for paying attention to other (possibly extraneous) sources of information. This may help explain why the experienced learners’ performance related to the study environment, while the novices’ results did not. Apparently, the level of attention needed for this particular kind of multimedia material was not high enough for experienced learners to exhaust their working memory resources.
Possible sources of extraneous information
In addition to questions about the actual auditory and visual backgrounds, the questionnaire included questions that offered us some insight into possible sources of extraneous background information. To begin with, the correlational coefficients indicating connections between the visual and auditory backgrounds (Table 6) show that the more often the background screens were turned on, the more often learners also reported background audio. While the question gave the learner the option to report screens other than TV (another computer, tablets, mobile phones, video consoles), the fact that the audio and video sources were related likely means that much of the visual distraction came from a TV. This assumption is supported by the fact that about 40% (134 min) of an average Estonian’s free time involves watching TV, as reported by Statistics Estonia (2010). In addition to conscious TV viewing, there may also be a fair amount of background TV (Gardner 2008). Still, this applies only to 38% of the learners, as 62% reported that background TV or other screens were never on while studying.
Answers to the question ‘How often were you alone in the room while studying?’ correlated significantly with descriptions of audio background (rho=.479 p=.000) across the sample. This suggests that, in addition to the TV, some background sounds are caused by other people in the room.
This paper investigated the auditory and visual characteristics of an authentic physical learning environment for 129 volunteers (85 novices, 44 experienced learners) studying the basics of guitar playing with the help of multimedia. The learners recorded and uploaded guitar exercises, which were assessed by two experts, and answered questions about their physical learning environment.
While the surroundings were described and perceived rather similarly, the main difference between the novices and the experienced learners was found to be the correlation between the reported frequencies of background sounds and the musical results. Those were moderately correlated in the case of experienced learners, while no significant correlation was found in the case of novice learners. This was explained by the limitations of working memory – novice learners have not yet acquired the automation of motor skills needed to play the guitar and thus do not have memory resources available to pay attention to extraneous auditory and visual information.
Advice for designing the multimedia study material for learning a musical instrument: The relationship between the physical learning environment and musical results may be partly controllable through the difficulty of the instructional material.
Limitations and suggestions for further research
The way a person perceives the environment and the extent to which it affects the learning results is highly individual and has been explained on the basis of different psychological constructs, such as cognitive style (e.g., Eyuboglu, Orhan 2011; Hayes, Allinson 1998), personality type (e.g., Dobbs, Furnham, McClelland 2011; Furnham, Strbac 2002) or musical intelligence (Aldalalah, Fong 2010a, 2010b). These individual differences – as well as gender differences (e.g., Armstrong 2011; Luik 2011; Mikk, Luik 2005) – were not taken into account in the present study.
As the sample for this paper consisted of volunteers, there were probably fewer problems with motivation than we would have to face if the tuition was part of a specific curriculum. In the latter case, adding elements from emerging gamification theories would be recommended (e.g., Lee, Hammer 2011; Muntean 2011).
The visual aspect when using multimedia for music instruction also deserves a dedicated study because novices have to pay attention to their hands while playing, which can result in missed visual material from the instruction and could also cause the split-attention effect described in CLT (Sweller 2010). The fact that the relationship between the auditory background and musical score was different for novice and experienced learners – but the visual background did not show a significant difference in the current study – may be caused by differences in measuring. Asking how often a person studied in silence does not have a proper counterpart in the visual domain. TV screens and other screens are just some of the potential visual distractions, albeit particularly common ones (Gardner 2008; Lin et al. 2011).
Using self-reporting to measure different characteristics of a physical environment may not seem the most reliable method, but it is used in cases where other methods are not applicable. For example, measuring the cognitive load still highly depends on subjective reports given after the learning experience (Brünken, Seufert, Paas 2010). Setting up objective tools to measure the actual physical characteristics of a learner’s home environment – without interfering with the real-life situation – can be challenging, although probably not impossible.
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