Computer-based composition and student engagement: A Hong Kong music education study

Computer-based composition and student engagement

Music composition through technology: Can popular and classical styles work together?

A year-long study in a Hong Kong secondary school explored how forty-four students used music software to compose in both classical and popular genres. The researchers wanted to know whether this approach kept students interested and helped them learn more deeply.

The project took place at a government-funded school with thirty iMac music workstations. Twenty-two Form 3 students (aged 14–15) worked on popular music, while twenty-two Form 4 students (aged 15–16) composed pieces in the baroque, classical, and romantic styles. At the end of the school year, all participants completed a retrospective survey that measured changes in their engagement and learning.

The study found three key points for curriculum designers:

• Students need lasting engagement with both classical and popular styles, not just one.
• A “de-composition and re-composition” teaching method worked well for both genres.
• Combining “old” and “new” musical styles through a cycle of engagement helped students understand both traditions better.

Why engagement matters in music education

Active participation in music brings cognitive, social, and emotional rewards. Yet many secondary students, especially in wealthier nations, report that music seems less valuable than other subjects, and their interest often fades as they move through school. One reason is that many programmes focus heavily on Western classical notation while leaving out other styles and creative activities. Even when teachers offer diverse content, students must find it personally meaningful. That inner sense of purpose and fulfillment is what researchers call positive engagement.

Psychologists describe student engagement as a mix of involvement, enthusiasm, and satisfaction. It grows from the interaction between motivation and active learning. If the learning feels difficult or out of touch, students easily drop out. Digital tools may offer a solution: they let learners work directly with sound, get immediate feedback, and explore music in ways that match their daily lives. This study asked how a computer-based composition cycle, moving between classical and popular music, could create that kind of meaningful involvement.

A history of composing at the computer

Researchers have studied computer-based composition since at least 1977, when Jeanne Bamberger used it to examine how musicians make melodic decisions. Since then, technology has become a central part of music teaching. Modern software makes it easy for students to generate ideas and adjust pitch, duration, tempo, and volume spontaneously. The computer acts as a musical sketchpad, turning sounds into real, editable material. Unlike pencil and paper, software gives instant audible confirmation, helping the composer hear the effect of every choice.

Popular music education and music technology have grown increasingly linked in recent years. According to one researcher, students live inside a “sound world” filled with diverse music, but schools often ignore it. A teacher or class that blends songwriting, recording, and production with instruments can bridge that gap. These approaches ask learners to create, perform, record, and produce original work, echoing how real musicians in pop genres operate. The current project builds on those ideas by letting students pick their own styles and references.

The recomposition method: Decompose and rebuild

Film composers often work by taking an existing piece apart and then putting it back together in a new form. A well-known example is Max Richter’s recomposition of Vivaldi’s Four Seasons. Richter layered and looped small fragments to refresh material that had become worn out from overuse. “There is Vivaldi DNA in all of this,” he said. “I kept the gestures and shapes, the textures and dynamics… bits of Vivaldi and bits of me daydreaming about the original – it was a way of having a conversation with Vivaldi.”

Using software, students can study earlier composers by doing something similar. They import audio or MIDI files from any period – baroque, classical, romantic, or twentieth-century – and then “decompose” that material into elements they want to use. By layering, reordering, and modifying these elements, they create a new hybrid composition that keeps certain traits of the original while adding their own voice. This process of decomposition and recomposition became the central pedagogy in the school project. For popular music, the steps were comparable: deconstruct a given song and then reassemble those pieces into a fresh arrangement.

“Richter aimed to create a new score, an experimental hybrid, that constantly references Vivaldi but also Richter and that is current but simultaneously preserves the original spirit of this great work.”

The three research questions

The study sought to answer two main questions:

1. Did students’ perceived engagement and learning change as they moved through the computer-based creative process, and did the change differ for those composing classical vs. popular works?
2. What do those differences imply for how educators design music curricula using technology?

How the study was set up

The school had powerful ICT support. Forty-four students chose “ICT in music” as their elective – a choice driven by convenience rather than random selection. Each class met for 60 minutes per week, and the school equipped the music room with 30 iMac computers running Sibelius (notation) and Logic Pro (sequencing / Digital Audio Workstation or DAW). Composition was judged directly from digital files held on those computers.

The year was divided into three stages:

1. In the first semester (September–December 2016), teachers collected information about each student’s background while teaching the basics of Logic Pro.
2. From January to March 2017, the focus shifted to music theory. Form 4 studied instrumentation, diatonic harmony, and forms such as binary and ternary using classical examples. Form 3 studied chord progressions and combo sections appropriate for popular music. Each 60-minute session included 45 minutes of presentation and 15 of individual help.
3. During April–May, every session became an open workshop. Students composed freely while teachers moved from one station to the next offering comments and fixing problems.

Different tasks appeared across the two semesters. The first was a prescriptive assignment with specific guidelines; the second was a free task with far broader limits. Using both types was intended to show whether engagement changes appear under either style of instruction or both.

Gathering data – the retrospective survey

Traditional pre- and post-tests can give misleading results. A phenomenon known as response-shift bias happens because, after instruction, students understand the subject well enough to know how much they actually did not know when they started. Consequently, conventional post-test scores might be equal or lower than pre-test scores, even though genuine improvement occurred. To avoid that problem, this study used a “post-pre” or retrospective self-assessment developed by the research team. At the very end of the project, students rated themselves twice on each of 35 questions covering 8 categories of engagement – once for how they were at the beginning, and a second time for how they were after finishing. They described perceived changes in knowledge, skill, enthusiasm, and future aspirations. Other medical and faculty education programmes have already tested and adopted this method because it produces a more consistent measuring tool.

Composers teach effectively

Trained music teachers led the classes, but they were assisted and supported throughout the project by professional composers and by university-based music education researchers. In addition, the entire ICT music curriculum was designed jointly by the teachers and the first author, who is himself both a research professional and a composer working in the computer-mediated domain.

Based on the theory, the framework includes: (1) activity values (interest, importance); (2) outcome values (creativity, expression, well-being); (3) collaborative learning (connectivity and belonging); (4) engaged learning (absorption, choice, autonomy); (5) inquiry-based learning (asking questions, finding information, problem solving, reflection, self-assessment); (6) perceived support (acceptance, assistance, meeting expectations); (7) perceived challenge and resiliency (effort, task difficulty, commitment, goals); and (8) competence (achievement, abilities, expectations). Students were asked to “compare yourself now with before the ICT in music classes began.” Knowing what they now know, they rated themselves on a two-step process: first deciding whether each statement was “not true for me” or “true for me,” then circling the rating on a 5-point Likert scale (0 = not at all true for me, 1 = not very true for me, 2 = sort of true for me, 3 = mostly true for me, 4 = very true for me). Two responses were given per item: one for before the programme and one at the end.

At the end of the post–pre survey, students wrote general comments about their experience with the creative process of composing popular and classical music in the computer-mediated environment. A thematic analysis categorized the written responses.

Results

Post–pre survey

The tables below display results from 23 retrospective assessment items. Overall, positive change was found in engagement and learning. Because the aim was to examine students’ perceptions of change, the numbers represent qualitative reflections, not purely descriptive data. Change is therefore reported descriptively; inferential statistics were not used for further comparisons. A mean score difference of 0.5 or higher was considered to indicate perceived change.

Table 1 shows students were strongly interested in learning popular music in the computer-mediated environment, with scores for Q1 rising from 2.21 to 3.05, a mean difference of 0.84 (>0.5). Responses to Q10 indicate both groups valued the importance of technology for composing, with mean differences of 0.89 and 0.7 (>0.5) respectively. Q18 suggests students learning popular music composition felt greater free choice when using technology, with scores increasing from 2.63 to 3.32, a mean difference of 0.69 (>0.5).

From Table 2, the classical music group reported a higher chance for creativity in composition with music technology (Q13), with a mean difference of 0.55 (>0.5). Music technology helped students in the popular group express themselves (Q20), mean difference 0.53 (>0.5). The popular music group also felt positive about using technology for composing (Q21), with a mean difference of 0.74 (>0.5).

Table 3 indicates that inquiry-based learning helped the popular music group make good progress in effective composition with technology (Q3), mean difference 0.89 (>0.5). Both groups demonstrated progress in becoming self-reflective learners by finding ways to improve composing skills with music technology (Q35), with mean differences of 0.85 (>0.5) and 0.65 (>0.5) respectively.

The results in Table 4 suggest both groups enjoyed composing directly in music software (Q4), with mean differences of 0.73 and 0.55 (>0.5), and the software provided the popular group a sense of accomplishment (Q5), mean difference 0.58 (>0.5). Both groups felt composing with music technology was worthwhile (Q17), with differences of 0.68 and 0.6 (>0.5).

Table 5 shows the popular music group was more engaged in trying their ideas with technology (Q6), mean difference 0.9 (>0.5), and felt a stronger connection to the music they listen to (Q7), mean difference 0.84 (>0.5). The classical music group indicated better focus (Q8), mean difference 0.6 (>0.5), and more absorption in the activity (Q12), mean difference 0.55 (>0.5). Overall, the popular music group had a higher level of engagement, and they thought about composing even when not actively doing it (Q19), with scores increasing from 1.53 to 2.37, a mean difference of 0.84 (>0.5).

Table 6 shows the popular music group perceived greater improvement in competence, with scores for Q9 rising from 1.63 to 2.42, a mean difference of 0.79 (>0.5).

The popular music group noted better connections with other musical experiences (Q34), mean difference 0.58 (>0.5). Table 7 addresses perceived support (acceptance, assistance, meeting expectations). Table 8 summarizes student comments on their experiences with computer-mediated composition of classical and popular music.

In Table 8, comments from classical music group students indicate the digital music course enriched their learning by building on music theory; they enjoyed learning from past great composers. They showed high engagement, tried to complete compositions, and developed ideas continuously with a sense of “flow.” From the popular music group, two students stated they became more confident because music references helped them overcome difficulties during the de-composing stage. They preferred rock style and spent significant time selecting drum patterns and fills in the re-composing stage.

Discussion

Sustainable engagement in learning music composition

Survey results indicated both groups felt positive engagement when composing classical and popular music with technology. These findings suggest sustainable engagement was achieved throughout the ICT music classes; students enjoyed the challenge of composing directly in the software and gained a sense of accomplishment. This engagement cycle helped sustain learning by providing ongoing impetus for a year-long project.

Regarding activity values (interest, importance), students were strongly interested in popular music in the computer-mediated environment (Q1, mean difference 0.84). Both groups saw technology’s value for composing (Q10, mean differences 0.89 and 0.7). The popular group felt greater free choice with technology (Q18, mean difference 0.69). In inquiry-based learning, both groups made good progress in effective composition (Q3, mean difference 0.89) and becoming self-reflective learners (Q35, mean differences 0.85 and 0.65). The de-composing and re-composing pedagogy proved effective. Perceived support was strong; both groups liked the challenge of composing directly on software (Q4, mean differences 0.73 and 0.55) and felt it was worthwhile (Q17, mean differences 0.68 and 0.6), suggesting a sense of accomplishment for both groups.

Student comments confirmed this: the classical group found the digital course extended their learning from theory; the popular group said they became more confident by easily dragging drum loops into software and learning parts they had never done before.

Overall, this study shows computer-mediated composition can sustain engagement and accomplishment in both classical and popular genres, concurring with Wise, Greenwood, and Davis (2011) who found similar high engagement and achievement. However, due to the small sample, caution is warranted in generalizing.

‘De-composing’ and ‘re-composing’ as composition pedagogy in classical and popular

Students’ comments indicated they grasped the value of ‘de-composing’ and ‘re-composing’ during both genres. The classical group stated this course taught them to start step-by-step, studying resources like Mozart and Beethoven. The popular group said listening to music references inspired them through struggles. These findings align with prior literature (Senyshyn and O’Neill 2017) on blending “old” and “new.” The idea of ‘de-composing’ and ‘re-composing’ can form a pedagogy where students select a classical or popular piece, analyze it, and re-create their own version in software.

Findings suggest this pedagogy in a computer-mediated setting encouraged engagement in both classical and popular music. Students could extract musical “DNA” elements (form, pitch, harmony, instrumentation, texture) and re-compose using Logic, adding contemporary elements like drum loops or re-orchestrating. This offers potential for sustained learning that extends beyond the classroom, with greater choice and autonomy. Interest in classical music, for instance, increased through learning Vivaldi’s works. This process is shown in Figure 1.

The model in Figure 1 illustrates that a ‘de-composing’ and ‘re-composing’ pedagogy can help students create, combine, and connect musical ideas. By studying the DNA of recorded music and re-creating new scores, students use software to engage with both old and new traditions.

Future research could explore links between students’ prior assumptions about genres, levels of engagement, accomplishment, and aspirations. Ongoing refinement of this pedagogy may further support student engagement and learning.

Acknowledgements

The authors thank the students who participated and the school administrators for their support.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Funding

This project was funded by the General Research Fund (GRF) Ref. no. 18603716, supported by the Research Grant Council (RGC) in Hong Kong.

Notes on contributors

Dr. Jason Chi Wai Chen is Assistant Professor in the Department of Cultural and Creative Arts at the Education University of Hong Kong.

Dr. Susan A. O’Neill is Dean, Faculty of Education, Simon Fraser University, Burnaby, Canada.

ORCID

Jason Chi Wai Chen: http://orcid.org/0000-0003-1506-6136

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