SELF-DIRECTED BLENDED LEARNING MODEL ON EMOTIONAL INTELLIGENCE AND ACHIEVEMENT

In the era of advancing globalization, developing emotional intelligence (EI) has become essential for adolescents to manage emotional stress, build social relationships, and adapt to changing environments. This study developed a Self-Directed Blended Learning (SDBL) model incorporating Intangible Cultural Heritage music to enhance students’ EI and academic achievement. The research was conducted in two phases: First, the SDBL model was designed and validated, consisting of five components (setting learning targets, planning, gathering information, online and offline learning activities, and post-lesson reflection). Five education experts evaluated the model using a 5-point Likert scale, yielding an average score of 4.84, confirming its pedagogical quality. Second, the teaching experiment was conducted at Taiyuan University of Science and Technology. A total of 112 non-music undergraduate students, who voluntarily enrolled in the Shanxi Intangible Cultural Heritage Music Appreciation course, participated in the study. The students were randomly divided into an experimental group and a control group, with 56 students in each. After the intervention, both groups completed post-tests to measure their emotional intelligence and academic achievement. Emotional intelligence was assessed using a revised 25-item version of the Schutte Self-Report Emotional Intelligence Test (SSEIT). Five experts evaluated the test’s content validity using the Item-Objective Congruence (IOC) method, with scores ranging from 0.6 to 1. Academic achievement was measured using test items from a nationally recognized Intangible Cultural Heritage (ICH) database maintained by the Chinese government. Five experts reviewed these items, and their IOC scores ranged from 0.8 to 1.0. The collected data were analyzed using t-tests and MANOVA. The results showed that the experimental group performed significantly better than the control group in both emotional intelligence and academic achievement. Additionally, a positive correlation was found between the two variables. The findings demonstrate that SDBL model incorporating Intangible Cultural Heritage music can enhance EI and academic achievement while simultaneously developing cognitive and emotional competencies, offering an innovative solution for preparing students to meet future challenges.

Authors

Institutions

Publication Details

Journal
Turkish Online Journal of Distance Education
Published
2026-04-01
DOI
https://doi.org/10.17718/tojde.1721236
Primary Topic
Emotional Intelligence and Performance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

SELF-DIRECTED BLENDED LEARNING MODEL ON EMOTIONAL INTELLIGENCE AND ACHIEVEMENT

Sirirat Petsangsri, Kanyarat Sriwisathiyakun, Fang Liu
Turkish Online Journal of Distance Education
Emotional Intelligence and Performance
article

SELF-DIRECTED BLENDED LEARNING MODEL ON EMOTIONAL INTELLIGENCE AND ACHIEVEMENT

Sirirat Petsangsri, Kanyarat Sriwisathiyakun, Fang Liu
article en

Abstract

In the era of advancing globalization, developing emotional intelligence (EI) has become essential for adolescents to manage emotional stress, build social relationships, and adapt to changing environments. This study developed a Self-Directed Blended Learning (SDBL) model incorporating Intangible Cultural Heritage music to enhance students’ EI and academic achievement. The research was conducted in two phases: First, the SDBL model was designed and validated, consisting of five components (setting learning targets, planning, gathering information, online and offline learning activities, and post-lesson reflection). Five education experts evaluated the model using a 5-point Likert scale, yielding an average score of 4.84, confirming its pedagogical quality. Second, the teaching experiment was conducted at Taiyuan University of Science and Technology. A total of 112 non-music undergraduate students, who voluntarily enrolled in the Shanxi Intangible Cultural Heritage Music Appreciation course, participated in the study. The students were randomly divided into an experimental group and a control group, with 56 students in each. After the intervention, both groups completed post-tests to measure their emotional intelligence and academic achievement. Emotional intelligence was assessed using a revised 25-item version of the Schutte Self-Report Emotional Intelligence Test (SSEIT). Five experts evaluated the test’s content validity using the Item-Objective Congruence (IOC) method, with scores ranging from 0.6 to 1. Academic achievement was measured using test items from a nationally recognized Intangible Cultural Heritage (ICH) database maintained by the Chinese government. Five experts reviewed these items, and their IOC scores ranged from 0.8 to 1.0. The collected data were analyzed using t-tests and MANOVA. The results showed that the experimental group performed significantly better than the control group in both emotional intelligence and academic achievement. Additionally, a positive correlation was found between the two variables. The findings demonstrate that SDBL model incorporating Intangible Cultural Heritage music can enhance EI and academic achievement while simultaneously developing cognitive and emotional competencies, offering an innovative solution for preparing students to meet future challenges.

Turkish Online Journal of Distance EducationVol. 27(2)
King Mongkut's Institute of Technology Ladkrabang (TH)
Sustainable cities and communities
Openalex Percentile: Top 20%
Emotional Intelligence and Performance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.