From Personalized Instructional Design to Student-Centered Instruction: The Mediating Roles of Data-Informed Decision Making and Adaptive Teaching Practices in Vietnamese Teacher Education

Dataset Title: “From Personalized Instructional Design to Student-Centered Instruction: The Mediating Roles of Data-Informed Decision Making and Adaptive Teaching Practices in Vietnamese Teacher Education”

 Description

This dataset contains survey responses collected from university faculty members working in teacher education programs in Vietnam. The dataset was developed to examine how personalized instructional design contributes to student-centered instruction through the mediating roles of data-informed decision making and adaptive teaching practices.

Teacher education institutions are increasingly expected to prepare future teachers capable of implementing learner-centered and technology-enhanced pedagogies. Despite growing interest in personalized learning, educational data use, and adaptive teaching, limited empirical evidence explains how these instructional capabilities operate together to support student-centered instruction in higher education. This dataset was created to address this gap by providing evidence from faculty members responsible for preservice teacher preparation.

Data were collected through an online survey administered between December 2025 and March 2026 across five public teacher education universities in Vietnam. After data screening and quality checks, 559 valid responses were retained for analysis.

The dataset contains demographic information together with responses to a multidimensional questionnaire measuring four major constructs:

1. Personalized Instructional Design (PID)
2. Data-Informed Decision Making (DIDM)
3. Adaptive Teaching Practices (ATP)
4. Student-Centered Instruction (SCI)

Personalized Instructional Design is operationalized through five dimensions:

• Learner Analysis
• Goal Alignment
• Adaptive Content Design
• Flexible Learning Pathways
• Personalized Assessment and Feedback

Student-Centered Instruction is operationalized through five dimensions:

• Active Learning
• Autonomy Support
• Collaborative Learning
• Differentiated Learning Support
• Student Voice and Choice

The complete dataset includes 66 variables consisting of:

• 4 demographic variables
• 62 survey items

All survey items were measured using a seven-point Likert scale ranging from 1 (Strongly Disagree) to 7 (Strongly Agree).

The dataset was designed for quantitative educational research and may be used for:

• Structural Equation Modeling (SEM)
• Partial Least Squares Structural Equation Modeling (PLS-SEM)
• Confirmatory Factor Analysis (CFA)
• Mediation analysis
• Educational leadership research
• Higher education research
• Personalized learning research
• Student-centered pedagogy research
• Digital transformation in education research

 Research Questions

The dataset was collected to address the following research questions:

• How does personalized instructional design influence data-informed decision making, adaptive teaching practices, and student-centered instruction among faculty members in Vietnamese teacher education universities?
• To what extent do data-informed decision making and adaptive teaching practices influence student-centered instruction?
• How do data-informed decision making and adaptive teaching practices mediate the relationship between personalized instructional design and student-centered instruction?

 Geographic Coverage

Vietnam

 Population

University faculty members teaching in teacher education programs at public education universities in Vietnam.

 Time Period

December 2025 – March 2026

 Sample Size

N = 559 faculty members

 Methodology

Cross-sectional survey design

Online questionnaire administration

Partial Least Squares Structural Equation Modeling (PLS-SEM)

 Variables Included

Demographic Variables

• Gender
• Age Group
• Teaching Experience
• Academic Discipline

Measurement Constructs

Personalized Instructional Design (26 items)

• Learner Analysis (5 items)
• Goal Alignment (5 items)
• Adaptive Content Design (6 items)
• Flexible Learning Pathways (5 items)
• Personalized Assessment and Feedback (5 items)

Data-Informed Decision Making (6 items)

Measures the extent to which faculty members use learner data, assessment evidence, and educational information to support instructional decision making.

Adaptive Teaching Practices (6 items)

Measures faculty members’ capacity to modify instructional strategies, pacing, learning activities, and support in response to learner needs and classroom conditions.

Student-Centered Instruction (24 items)

• Active Learning (5 items)
• Autonomy Support (5 items)
• Collaborative Learning (5 items)
• Differentiated Learning Support (5 items)
• Student Voice and Choice (4 items)

Data Quality Procedures

Several quality assurance procedures were implemented prior to analysis:

• Removal of incomplete questionnaires
• Screening for excessive missing values
• Identification and removal of straight-line responses
• Consistency checks across responses

A total of 587 questionnaires were initially collected. Twenty-eight responses were excluded during data cleaning, resulting in a final sample of 559 valid cases.

 Ethical Considerations

Participation was voluntary and informed consent was obtained from all participants before data collection. Responses were anonymized prior to analysis. No personally identifiable information is included in the released dataset.

Ethical approval was granted by Vietnam National University Hanoi under Decision No. 6198/QĐ-ĐHQGHN dated 24 December 2024.

 Funding

This research was supported by Vietnam National University Hanoi under the science and technology mission:

“Research on a Digital Pedagogical Competency Framework for University Faculty in Digital Education Ecosystems”

Project Code: QG.25.70

 License

Creative Commons Attribution 4.0 International (CC BY 4.0)

 Keyords

Teacher Education; Personalized Instructional Design; Data-Informed Decision Making; Adaptive Teaching Practices; Student-Centered Instruction; Higher Education; Vietnam; Educational Innovation; Digital Transformation; PLS-SEM; Faculty Development; Learning Analytics