A Cognitive Framework for On-line Music Education

Students’ Performance in On-line Listening Activities

in a Blended Post-secondary Music Course

Patricia Boechler

, Mary Ingraham

, Luis Fernando Marin

, Erik deJong

and

Brenda Dalen

Department of Educational Psychology, University of Alberta, Edmonton, Canada

Department of Music, University of Alberta, Edmonton, Canada

Keywords: Music Education, On-line, Listening, Cognitive Framework, Higher Education.

Abstract: This paper describes a cognitive framework for designing on-line listening activities for students in post-

secondary music courses. Drawing on music cognition and knowledge acquisition theories, technology-

based listening activities were developed as supplemental to classroom-based activities. The study sample

consisted of fifty-nine post-secondary students in a World Music course. Before engaging in the listening

activities, students completed four pre-activity surveys: 1) general demographics (e.g., program, year in

program, gender, age), 2) a music experience survey (non-credit music experience), 3) a self-regulation

questionnaire (SRQ) and, 4) a Computer Experience Questionnaire. Students then completed two on-line

t is not easily enacted in the large classroom due to noise and other distractions, and to the lack of time for

students with higher self-regulation scores took significantly less time to complete the on-line listening

activities than those with lower self-regulation. However, as predicted by Honing’s (2009) music cognition

theory, students’ levels of music experience were not related to students’ efficiency in completing the

activities; nor was their computer experience or their levels of self-regulation.

1 CONTEXT AND

BACKGROUND

The question that animates this study is how to

support students in post-secondary music courses to

develop the skills to listen to music and to

communicate what they hear. Close listening

requires active participation and its practice cannot

be taken for granted; neither can an understanding of

what one is hearing be assumed to be universal. In

this paper, we explore the cognitive processes

required to make explicit the musical elements that

are understood implicitly and we consider ways of

using technologies to support close listening

practice. In articulating this question, we are

cognizant of the issues facing undergraduate music

education: pressures on the amount of time available

for face-to-face contact and increasing competition

for the inclusion of a wide range of musical styles in

order to engage students in their learning. Such

issues stem at least in part from the apparent

democratization of post-secondary classrooms in

which multiple interests, varied cultural

backgrounds, and uneven prior experiences with

musical materials compete for time and place.

The study of music suffers greatly in the

contemporary classroom. In response to the need to

incorporate a cross-section of musical styles, genres,

and contexts in our institution, we have redesigned

the music history curriculum to emphasize core

competencies rather than follow traditional models

of chronological, narrative, and style-specific

curricula. Gone are the days of presenting a survey

of 400 years of (only) classical music to first year

music majors, and in its place are courses that teach

students to listen – to traditional, popular, and

classical musical works in the context of their

creation and performance and as comprehensive

exemplars of particular times and places. Close

listening, arguably the foundation of all music study,

serves as the basis for this redesigned curriculum,

and it strives to engage students more deeply in the

musical examples themselves, allowing instructors

to work with students more meaningfully to develop

the skills to communicate what they hear and how

they understand its significance. A shift to

competency-based learning and the premise of

complex cultural contexts has required a

reconsideration of what and how we present

materials for study in and outside of the classroom.

Boechler P., Ingraham M., Marin L., deJong E. and Dalen B..

A Cognitive Framework for On-line Music Education - Students’ Performance in On-line Listening Activities in a Blended Post-secondary Music Course.

DOI: 10.5220/0005429300450051

In Proceedings of the 7th International Conference on Computer Supported Education (CSEDU-2015), pages 45-51

ISBN: 978-989-758-107-6

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

The primary challenges in teaching music

listening in 21

century post-secondary

environments are related to the time and space of

student-instructor interaction. Contemporary

students, for whom music is largely background to

their daily activities – a soundtrack, if you will –

first need encouragement to move music to the

foreground of their thoughts, then to learn to

describe what they are hearing, and finally to

develop the critical thinking capacities to imagine

cross-cultural meaning. First year classes of 50-250

students are particularly difficult places to focus

such individual and experiential listening and

discussion: competing sounds and movement in

classrooms, lack of familiarity with the diversity of

musical styles, and the rigid timetabling of

institutional programs result in very little music

being played in class and very little time allocated to

discussion and feedback. Lecture-style presentation

and testing based on the memorization of selected

(i.e., pre-programed) musical concepts is therefore

the norm. And despite myriad opportunities for

listening outside the classroom, few students take

advantage of it unless the instructor is involved.

With guidance, early stages of close listening such

as are considered here assists students in

understanding and connecting specialized

terminology to music, in comprehending graphic

notation and other documentation of cultural matter,

and in hearing the complexity of formal structures

and the organization of sounds across musical time.

Lastly, a general discomfort with expressing

subjective responses frequently keeps the majority

of students from participating in open discussion. As

Long et al (2011) report, increased opportunities for

peer-observation and interaction assist students in

developing the skills for future involvement in

musical activities (696).

Studies in music cognition (Honing, 2009)

indicate that individuals possess far greater

understanding of what they hear than they believe,

suggesting that what is needed in music education is

to find appropriate methods to assist students in

bringing their innate knowledge to the surface.

In seeking to develop such confidence in

students, the challenges of classroom learning must

be addressed. Therefore, in this study we propose to

resolve some of them by employing educational

technologies and a blended delivery format to extend

the classroom and invite individual interaction with

materials, guiding students into group discussions of

ever more complex cultural environments. The

problems of insufficient class time to listen to longer

musical examples and limited discussion

opportunities are met with multiple asynchronous

activities; the distractions of classroom sounds and

movement are resolved through the potential for

direct input of the music through headphones; the

challenge of a vast historical and stylistic content is

resolved by increasing the quantity and type of

materials included in activities; and the enhanced

capabilities of technology allow music to be situated

alongside other cultural objects, including video and

expressions of other art forms, to establish its place

within specific cultural contexts. Finally, in our

multicultural classrooms, the lack of a common

ground of musical experience is increasingly

evident; the activities created in the multiple

technical and musical contexts emphasized in this

study are designed to allow students to move from

the personal to the communal, and to come full

circle back towards a common understanding of the

role of music in their own world.

2 THE COGNITION OF

LISTENING

To ensure a solid cognitive foundation for our

listening activities, we reviewed the literature on

music cognition. Honing (2009) contends that

humans possess an inborn ability to hear certain

patterns in music such as the meter or beat of the

music as well as to distinguish one melody from

another (relative pitch).

Starting from infancy, humans experience the

music of their own culture and develop their implicit

knowledge of it over time, relying on these innate

abilities, become adept at distinguishing common

patterns and aspects of music that are distinct to their

cultural setting. However, Honing asserts that this

accumulated knowledge is not accessible to the

conscious cognitive system. People are generally

unaware they possess these skills and are not able to

consciously draw on or report such knowledge. This

is referred to as implicit knowledge.

Honing also contends that people with no training

in music are not substantially less able to detect

these basic elements than those who possess

considerable musical expertise. The difference is

that this knowledge is explicit in experts, that is,

they are aware they possess it, and are able to report

such knowledge, allowing them to share their

interpretations of the music to which they are

listening. Hence, music education for introductory

students should provide scaffolded listening

opportunities which help them become aware of

CSEDU2015-7thInternationalConferenceonComputerSupportedEducation

their implicit knowledge, thereby transferring it to

explicit and sharable knowledge.

3 A COGNITIVE FRAMEWORK

FOR GUIDED LISTENING

From a theoretical perspective, the need to shift

implicit knowledge to explicit knowledge most

closely aligns with the concepts described in

Karmiloff-Smith’s Representational Redescription

Model (RR Model) of knowledge acquisition

(Karmiloff-Smith, 1992). Through a process she

refers to as redescription, implicit knowledge is

transformed into explicit knowledge through four

phases: Implicit Level (I), Explicit Level One (E1),

Explicit Level Two (E2), and Explicit Level Three

(E3).

In Implicit Level (I), individuals possess

knowledge they have accumulated over time that is

not available to their conscious awareness but allows

them to respond correctly to external stimuli in their

environment. In introductory music courses, this

would be evidenced when students are able to

answer questions about basic musical elements such

as meter (e.g., is this music metric or non-metric?)

The second level, Explicit Level One (E1),

furthers the process of redescription when the

student begins to be able to make comparisons

between two pieces of music.

Students are then scaffolded into Explicit Level

Two (E2) through the process of feedback during

their listening experience. Through a sequence of

listening to multiple pairs of pieces and being

tutored on the terms used to describe the differences

they are hearing, students begin to acquire the

terminology that will help them discuss the musical

nuances they are able to detect.

In Explicit Level Three (E3), the students are

conscious of and able to consider multiple forms of

the material. They can hear differences in the music,

describe these differences with commonly

understood terms or diagrams and develop the skills

needed to follow musical notation. Finally, students

are also able to contextualize this material in a

specific cultural space and time. Elements of the

music can be tied to the socio-political influences of

a specific point in history (A sample statement might

be: “The [melody, harmony, rhythm, or timbre] you

hear at the beginning of this piece is very typical of [

a particular person, place, genre, etc.] and reflects

the [music, dance, culture, politics, etc.] of that

time.) At E3, verbal interactions with others is

critical toward developing a complex understanding

of how these multiple forms of information fit

together.

4 TECHNOLOGIES TO DELIVER

THE COGNITIVE

FRAMEWORK FOR GUIDED

LISTENING

Each of these four phases suggests activities that

students must experience to move through each

phase. To create our supplemental on-line listening

activities, we sought the technologies that could best

deliver each set of activities.

We began our guided listening activities at

Implicit Level (I) using the software Articulate

Storyline which allows for the creation of interactive

presentations with quiz features and audio capacity.

Within each slide, students can click, hover over, or

drag any object to trigger an action. With Articulate,

we created multiple auditory presentations of music

pairs that allowed students to listen to, respond, and

receive new information, listen again, respond, etc.

until they were able to detect specific musical

elements such as meter and rhythm. This activity

gives students practice in drawing on the implicit

knowledge they already possess to detect basic

elements of the music they hear.

The activity is done independently, and with

individual results so students can develop their

listening skills without being inhibited by being

observed. Students can complete some of the

activity and return later to complete the rest. The

Articulate Storyline interface is visually simple to

allow students to attend to the music examples

without distractions from a complex visual interface.

The features are simple to use and limited in number

to encourage the focus to be on the listening rather

than the interface itself. Across the two Articulate

modules, students were presented with increasingly

more difficult questions. For incorrect answers,

students received feedback and suggestions for

listening (e.g., “That is incorrect. Listen again, is

there a beat you can tap your finger to?”). The

Articulate activities represent the first set of on-line

listening activities that we developed and targets

transitions from Implicit Level (I) to Explicit Level

one (E1).

To transition from E1 to E2, opportunities to

begin to label and share listening experiences is

necessary. To promote this transition, we suggest

instructor guided discussion in smaller groups

ACognitiveFrameworkforOn-lineMusicEducation-Students'PerformanceinOn-lineListeningActivitiesinaBlended

Post-secondaryMusicCourse

Figure 1: Sample of Articulate activity - identifying

musical elements: listen and answer questions on

differences in melody, harmony, rhythm, timbre, and

texture.

delivered via a conferencing system such as

Elluminate or Adobe Connect. This will allow for

students to ask questions and give responses in much

smaller groups than in the large classroom without

the need for multiple physical break-out spaces.

For the final transition to E3, experiencing

multiple forms of information is needed to help

students contextualize their listening experience in

the cultural and socio-political influences of the time

in which the music was composed. To accomplish

this last goal, we have developed a virtual world

using OpenSim with images, videos, audio clips and

settings that replicate the space and time we are

referencing. Students are represented as avatars in

the virtual world and can move through the space

while conversing with peers. In the virtual world,

students engage in a guided discussion as they view

and interact with all of these materials.

5 STUDENT CHARACTERISTICS

Given the diversity of students within introductory

music courses, we felt it necessary to explore the

potential impact of key student characteristics,

including prior music and computer experience and

levels of self-regulation.

Recent studies by Demorest et al (2008) and

Morrison et al (2013) suggest that prior music

training is not a barrier to successful study of

unfamiliar music. However, both of these studies

explore the enculturation effect – that is, the effect

of culture-specific listening and performing on

memory, or students’ ability to remember unfamiliar

music - while Long et al (2011) determine that prior

experience is statistically remarkable in students’

ability to perform. These studies do not explore the

skill of listening for understanding and

communicating cultural context as we are attempting

here. Rather, with this project, we seek to determine

whether music experience impacts a student’s ability

to listen to any musical style or genre in addition to

examining how they listen and respond to its

interpretation.

Honing (2009) asserts that levels of prior

experience with music training should make little

difference to one’s ability to listen and comprehend.

To determine if Honing’s position on music

cognition is a viable basis for the design of on-line

listening activities, we conducted premeasures on

prior music experience for consideration in data

analysis.

Boechler, Dragon and Wasniewski (2015)

suggest that levels of computer experience can be

related to performance levels on a variety of digital

tasks and, given that our pedagogical framework is

implemented through digital tasks, we also

premeasured prior computer experience.

Finally, we premeasured levels of self-regulation

because Long, Hallam, Creech, Gaunt and

Robertson (2011)

assert that, “The cyclic process of

self-regulated learning has been identified as a

predictor of achievement in musical skill acquisition

and musical performance” (p. 683).

6 METHODS

In Fall 2014, we ran a pilot project of just the

Articulate Storyline activities and the OpenSim

activities. Our first analysis of this data focused on

the students’ perceptions of these activities and

investigated if prior traits (self-regulation levels) and

experiences (prior computer and music experiences)

were related to their perceptions of the activities

(Boechler, Ingraham, Marin Fernando, Dalen, and

deJong, 2015). Students indicated that they enjoyed

the activities and that they felt the activities had

enhanced their listening and learning experiences.

The current paper describes the analysis of the

pilot study log data of the Articulate Storyline

activities to determine the students’ performance

(time on task, the number of page revisits and the

percentage of correct responses) and whether any of

these performance measures were related to their

previously mentioned traits and prior experiences.

6.1 Participants and Procedures

Eighty-six post-secondary students took part in the

study. Due to incomplete datasets, fifty-nine were

included in the final analyses.

CSEDU2015-7thInternationalConferenceonComputerSupportedEducation

Before the listening activities commenced ,

several pre-measures were collected: 1) General

demographics (e.g., program, year in program,

gender, age), 2) a music experience survey (non-

credit music experience), 3) a self-regulation

questionnaire (SRQ) (Brown, Miller, and

Lawendowski, 1999), 4) a Computer Experience

Questionnaire. (Boechler, Leenaars, and Levner,

2008).

The Music Experience Questionnaire has nine

items relating to previous musical training, including

specific questions on learning a musical instrument,

singing in a choir, taking dance lessons, studying

music theory or history, or reading musical notation.

For this study, we also ask whether the student had

any previous knowledge of the subject of the class,

world music. These were all yes or no questions.

The Computer Experience Questionnaire is

comprised of three measures: 1) the Software

Recognition Test (SRT), which is a measure of

general exposure to computer applications and

digital materials, 2) the Educational Activities

Checklist (EAC), and 3) the Recreational Experience

Scale (RES). The SRT requires students to check off

the software titles they recognize on a checklist of

forty titles, twenty of which are actual titles, twenty

of which are foils to control for guessing. The EAC

asks students to indicate which education-related

computer activities students have carried out (e.g.,

writing html code, using a formula in a spreadsheet,

using a library database). The REC asks students to

indicate, on a five point likert scale, the range of

hours per week they spent playing video games or

social networking in Elementary, Junior High, High

School and University.

The Self-Regulation Questionnaire has 63 items

on a 5 point Likert scale such as, “I usually keep

track of my progress toward my goals”. Self-

regulation is the ability to create a plan, then execute

and make adjustments to it in order to reach one’s

goal.

Students were then asked to complete the on-line

listening activities that were supplemental to their

face-to-face classroom activities. These activities

were on the students’ own schedule and were self-

paced. They completed two sets of four listening

activities, one set about meter and one about rhythm,

delivered via Articulate Storyline at approximately

the mid-point in the semester, that is, beginning in

Week 7 of a 13-week course.

As our outcome measures, we collected log data

from the Articulate Storyline software as the

students completed the listening activities. The

performance measures were: 1) the overall time on

task, 2) the number of page revisits and, 3) score for

correct answers. We considered these three

dependent variables as indicators of the students’

efficiency in completing the tasks. We also wished

to determine if any prior traits or experiences the

students brought into these activities were related to

their efficiciency in completing the tasks. We

addressed the following sets of research questions:

Music Ability:

1) Is there a difference in the total time on task as

a function of the students’ level of prior music

experience (High, Low)?

2) Is there a difference in the number of pages

revisited as a function of the students’ level of

prior music experience (High, Low)?

3) Is there a difference in the percentage of

correct answers as a function of the students’

level of prior music experience (High, Low)?

Computer Experience:

4) Is there a difference in the total time on task as

a function of the students’ level of prior

computer experience (High, Low)?

5) Is there a difference in the number of pages

revisited as a function of the students’ level of

prior computer experience (High, Low)?

6) Is there a difference in the percentage of

correct answers as a function of the students’

level of prior computer experience (High,

Low)?

Self-Regulation

7) Is there a difference in the total time on task as

a function of the students’ level of Self

Regulation (High, Low)?

8) Is there a difference in the number of pages

revisited as a function of the students’ level of

Self Regulation (High, Low)?

9) Is there a difference in the percentage of

correct answers as a function of the students’

level of Self Regulation (High, Low)?

7 RESULTS

To detect any differences in performance between

different levels of each of the independent variables

(prior music experience, prior computer experience

and the SRQ) , for each dependent variable, (time on

task, number of page revisits, number of correct

answers) the sample was divided into two groups.

Students were categorized as High on each measure

if their score was above the mean for that measure.

ACognitiveFrameworkforOn-lineMusicEducation-Students'PerformanceinOn-lineListeningActivitiesinaBlended

Post-secondaryMusicCourse

Students were categorized as Low on each measure

if their score was below the mean for that measure.

An independent samples T-test was then conducted

for each combination of independent and dependent

variables.

7.1 Music Experience

Question #1: Is there a difference in the total time

on task (minutes) as a function of the students’ level

of prior music experience (High, Low)? The T-test

indicated there was no difference between high and

low music experience groups on total task time

(High group (n = 37, M = 6.22 , SD = 3.40 ), and

the Low group (n = 22, M = 7.97, SD = 6.60 ), t(1,

27.77*) = 1.16 , p = .256 (* adjusted df for unequal

variances). In other words, students with extensive

music abilty did not complete the listening activities

any faster than those with little experience.

Question #2: Is there a difference in the number

of pages revisited as a function of the students’ level

of prior music experience (High, Low)? The T-test

showed that the differences in the number of

revisited pages between the High group (n = 37, M

= 3.59, SD = 12.68), and the Low group (n = 22, M

= 3.54, SD = 7.73) was not significant, t(1, 57) = -

.016, p = .987.

Question #3: Is there a difference in the score for

correct answers as a function of the students’ level

of prior music experience (High, Low)? No, prior

music experience did not help or hinder students in

achieving correct answers in the on-line listening

activities.(High group (n = 37, M = 154.32, SD =

77.98), and the Low group (n = 22, M = 129.40, SD

= 47.55), t(1,57) = -1.35, p = .181.

7.2 Computer Experience

Question #4: Is there a difference in the total time

on task as a function of the students’ level of prior

computer experience (High, Low)? The T-test on

group computer experience yielded no significant

differences (High group (n = 35, M = 7.07 , SD =

5.30), and the Low group (n = 24, M = 6.58, SD =

4.25), t(1,57) = -.374 , p = .710 . It did not matter if

students were proficient or not with computers. Low

computer skills were not related to increased time to

complete the listening activities.

Question #5: Is there a difference in the number

of pages revisited as a function of the students’ level

of prior computer experience (High, Low)? Level of

computer experience did not produce a difference in

the number of pages students revisited, (High group

(n = 35, M = 1.97, SD = 6.61), and the Low group

(n = 24, M = 5.92, SD = 15.23), t(1, 28.99*) = 1.19,

p = .242.

Question #6: Is there a difference in the score for

correct answers as a function of the students’ level

of prior computer experience (High, Low)? Again,

computer experience did not seem to play a role in

students’ abilities to answer the questions correctly

(High group (n = 35, M = 139.63 , SD = 49.91 ),

and the Low group (n = 24, M = 152.91, SD =

90.35), t(1,57) = .725 , p = .471.

7.3 Self Regulation

Question #7: Is there a difference in the total time

on task as a function of the students’ level of Self

Regulation (High, Low) ? No, no matter the

students’ level of self-regulation, they did not

perform faster or slower. (High group (n = 27, M =

5.96, SD = 3.76), and the Low group (n = 32, M =

7.64, SD = 5.58), t(1,57) = 1.33, p = .190. Self-

regulation is the ability to create a plan, then execute

and make adjustments to it in order to reach one’s

goal.

Question #8: Is there a difference in the number

of pages revisited as a function of the students’ level

of Self Regulation (High, Low)? No difference was

detected in the number of revisited pages as a result

of self-regulation scores, (High group (n = 27, M =

4.85, SD = 15.01), and the Low group (n = 32, M =

2.50, SD = 5.49), t(1, 32.89*) = -.765, p = .450.

Question #9: Is there a difference in the score for

correct answers as a function of the students’ level

of Self Regulation (High, Low)? The T-test was not

significant in this case either. (High group (n = 27,

M = 149.44, SD = 95.09), and the Low group (n =

32, M = 141.31, SD = 35.41), t(1, 32.07*) = -.420 , p

= .677.

8 DISCUSSION

The listening activities described in this paper were

a preliminary version of a set of listening activities

for music students based on Honing’s (2009)

concepts of music cognition and Karmiloff-Smith’s

(1992) Representational Redescription Model (RR

Model) of knowledge acquisition.

Honing asserts that novice listeners are almost as

good as expert listeners in detecting changes in basic

musical elements but simply lack the awareness of

their implicit knowledge and the music vocabulary

to share their perceptions with others.

Our results support Honing’s assertion, in that

prior music experience was not related to students’

CSEDU2015-7thInternationalConferenceonComputerSupportedEducation

ability to execute the listening tasks. Students with

low music experience were not significantly

different in the time they took to complete the task

nor in the number of page revisits or their scores for

correct answers from those students with high music

experience. Given that, unlike traditional music

courses, we created listening activities that were

designed to tap into implicit knowledge and we

detected no difference in students with high or low

music experience, we suggest that the choice of

Honing’s cognitive theory as a viable basis for

supplemental listening activities is supported.

The Representational Redescription Model

provides an implementable framework, through the

suggested types of activities, for actually moving

learners from Implicit to Explicit and sharable

knowledge.

Morrison et al (2013) suggest designing music

curricula using “paired same-different discrimina-

tions … may be more sensitive to early stages of

music learning” (p. 370), however, given the impact

of this study on student satisfaction and success, we

are proposing classroom adoption of this model for

listening-focused music courses, adapted according

to the content of the course (popular music, classical

music, other culture-specific music and so on) and

the specific listening outcome required. Listening for

musical structure, comparing melodic variation, etc.

are also suited to this method.

9 CONCLUSIONS

Large introductory post-secondary courses often

produce challenges to instructors to provide

accessible and meaningful experiences for students.

In the case of music courses, listening is a critical

activity that is not easily enacted in the large

classroom due to noise and other distractions, and to

the lack of time for repeated listening and discussion

of the listening experience.

The diversity of students within these classes is

also a concern as prior traits and experiences may

influence their ability derive any knowledge from

the degrade classroom listening activities.

The series of technology-enhanced listening

activities which we have designed are theoretically-

derived and do not disadvantage students with lower

skills or less experience than other students.

Therefore, our on-line listening activities show

promise for alleviating some of the limitations of

teaching music, particularly close listening skills, in

large post-secondary classrooms.

REFERENCES

Boechler, P, Dragon. K, and Wasniewski, E in press.

Digital Literacy Concepts and Definitions:

Implications for Educational Assessment and Practice.

Special Issue. International Journal of Digital

Literacy and Digital Competence.

Boechler, P, Ingraham, M, Marin Fernando, L, Dalen, B,

and deJong, E. 2015. Making the Implicit Explicit:

Music Listening, Blended Delivery and the

Representational Redescription Model. International

Journal for Cross-Disciplinary Subjects in Education

(IJCDSE), vol. 6, no.1, pp.2095-2105.

Boechler, P, Leenaars, L and Levner, I 2008. Recreational

vs. Educational Computer Experience: Predicting

Explicit and Implicit Learning Outcomes During a

Website Search. In K. McFerrin et al. (Eds.),

Proceedings of Society for Information Technology

and Teacher Education International Conference,

Chesapeake, VA: AACE. (pp. 2499-2501).

Brown, JM, Miller, WR, and Lawendowski, LA 1999.

The Self-Regulation Questionnaire. In L. VandeCreek

and T. L. Jackson (Eds.), Innovations in clinical

practice: A source book. Sarasota, FL: Professional

Resource Press. (Vol. 17, pp. 281-289).

Demorest, SM, Morrison, SJ, Jungbluth, D, and Beken,

MN 2008. Lost in translation: An Enculturaltion

Effect in Music Memory Performance. Music

Perception: An Interdisciplinary Journal, vol. 25, no.

3, pp. 213-223.

Honing, H 2009. Musical Cognition: The Science of

Listening. Transaction Publishers, Rutgers, NJ.

Karmiloff-Smith, A 1992. Beyond modularity: A

developmental perspective on cognitive science. MIT

Press, Cambridge, MA.

Long, M, Hallam, S, Creech, A, and Roberston, L 2011.

Do prior experience, gender, or level of study

influence music students’ perspectives on master

classes? Psychology of Music, vol. 40, no. 6, pp.683–

699.

Morrison, SJ, Demorest, SM, Campbell, PS, Bartolome,

SJ, and Roberts, JC 2013. Effect of Intensive

Instruction on Elementary Students’ Memory for

Culturally Unfamiliar Music. Journal of Research in

Music Education, vol. 60, no. 4, pp. 363-374.

ACognitiveFrameworkforOn-lineMusicEducation-Students'PerformanceinOn-lineListeningActivitiesinaBlended

Post-secondaryMusicCourse