Architectural Visualization Student Performance Dataset- Summer 2025 (Interdepartmental Study: MCT and ESDM)

Architectural Visualization Student Performance Dataset – Summer 2025 (Interdepartmental Study: MCT and ESDM)

1. Overview

The Architectural Visualization Student Performance Dataset (Summer 2025) presents a detailed record of student performance in two sections of the Architectural Visualization course offered at Daffodil International University (DIU), Bangladesh.

Although the sections were conducted under different academic programs, both followed the same syllabus, course structure, and evaluation process. The course was taught and coordinated by S. M. Monowar Kayser, Lecturer, Department of Multimedia and Creative Technology (MCT), DIU.

The dataset combines the results of students enrolled in:

B.Sc. (Hons) in Environmental Science and Disaster Management (ESDM)

B.Sc. in Multimedia and Creative Technology (MCT)

Its purpose is to support comparative studies, institutional research, curriculum evaluation, and broader educational data analysis.

2. Dataset Composition

Attribute

Description

Total Records

76 student entries

Departments Included

Environmental Science and Disaster Management (ESDM) and Multimedia and Creative Technology (MCT)

Course Title

Architectural Visualization

Course Codes

ESDM 409, 0212-323

Semester

Summer 2025

Credit Hours

3.00

Instructor

S. M. Monowar Kayser, Lecturer, Department of MCT, DIU

Institution

Daffodil International University, Bangladesh

Each record provides a complete academic profile, including attendance, class tests, project-based work, midterm and final exam marks, and the final course grade.

3. Data Structure and Variable Description

Column Name

Description

Type / Format

SL

Serial number (1–76)

Integer

Department

Student’s department (ESDM / MCT)

Text

Course Code

Course identification code

Text

Student ID

Unique registration number

Text

Name of Student

Full name of the student

Text

Attendance (out of 7)

Attendance marks

Numeric

CT-1 / CT-2 / CT-3 (out of 15)

Class-test scores

Numeric

Class Test (out of 15)

Average score from class tests

Numeric

Presentation / Viva (out of 8)

Marks for presentation or viva

Numeric

Assignment (out of 5)

Assignment marks

Numeric

Mid Term (out of 25)

Midterm exam marks

Numeric

Final Exam (out of 40)

Final exam marks

Numeric

Grand Total (out of 100)

Combined total marks

Numeric

Final Grade

Letter grade following DIU grading system

Text

Performance Category

Performance level: High, Moderate, Low, or Very Low / Incomplete

Text

4. Performance Categories

Grade Range

Category

Description

A+, A, A−

High Performance

Excellent mastery of course objectives

B+, B, B−

Moderate Performance

Consistent and satisfactory progress

C+, C, D

Low Performance

Limited achievement; improvement needed

I, F

Very Low / Incomplete Performance

Incomplete or failing outcome

This classification enables consistent comparison across departments and helps identify areas where students may need additional support.

 5. Applications

The dataset can be used for:

Analyzing patterns of student performance

Evaluating curriculum effectiveness

Educational data mining and predictive modeling

Institutional benchmarking and policy design

Visualization and academic research projects

Keywords

Architectural Visualization, Student Performance, Educational Data, Learning Analytics, Academic Performance, Higher Education, Assessment and Evaluation, Data Science in Education, Architecture Education, Design Education, Daffodil International University, Bangladesh, University Students, STEM Education, Education Research, Academic Dataset