DOI: 10.5281/zenodo.17467667
title: A Longitudinal Study of Student Performance and Curriculum Evolution Across Repeated Course Offerings Under the Same Instructor in Creative-Technology Education (Fall 2023 – Summer 2025)

description: A Longitudinal Study of Student Performance and Curriculum Evolution Across Repeated Course Offerings Under the Same Instructor in Creative-Technology Education (Fall 2023 – Summer 2025)

 

Abstract

This dataset presents a three-year longitudinal record of student performance across repeated course offerings in the field of creative-technology education.Collected from the Department of Multimedia and Creative Technology (MCT) at Daffodil International University (DIU), Bangladesh, it includes 755 anonymized student-course entries covering both theory and laboratory modules taught between Fall 2023 and Summer 2025.

The dataset captures performance metrics under a single instructor, S.M. Monowar Kayser, ensuring uniformity in teaching methodology and grading policy.It provides a reliable empirical foundation for exploring curriculum evolution, grading consistency, learning outcomes, and AI-based educational analytics in multimedia and creative disciplines.

 

Instructor

S.M. Monowar KayserLecturer, Department of Multimedia and Creative Technology (MCT)Daffodil International University (DIU), Dhaka, Bangladeshkayser.mct@diu.edu.bd

 

Dataset Overview






Attribute



Description





Institution



Daffodil International University (DIU)





Department



Multimedia and Creative Technology (MCT)





Semesters



Fall 2023, Spring 2024, Fall 2024, Spring 2025, Summer 2025





Course Families



8 (2D Animation, Character Animation, Lighting & Rendering, Audio and Video Streaming & Editing, Architectural Visualization, Design Principles, Landscape Simulation, Physics-Based Animation)





Course Instances



41 unique sections (theory and lab)





Instructor Consistency



All courses taught and assessed by the same instructor





 

 

 

Assessment Framework

Theory Courses






Component



Weight



Description





Attendance



7 %



Regular participation in class activities





Quizzes / Single Project (3×15 → Average)



15 %



Short assessments or a creative mini-project





Assignments



5 %



Individual design or written work





Presentations



8 %



Visual or oral demonstrations of concepts





Midterm Exam



25 %



Mid-semester assessment





Final Exam



40 %



Comprehensive final evaluation





Total = 100 marks (converted to percentage).

 

 

Laboratory Courses






Component



Weight



Description





Attendance



10 %



Lab participation and engagement





Lab Performance



25 %



Continuous assessment of technical skill





Lab Report / Assignment



25 %



Documentation of projects or tasks





Lab Final Exam



40 %



Final practical demonstration





Total = 100 marks (converted to percentage).

 

 

Data Composition

Each row represents one student record per course per semester.All identifiers are synthetic (STU-YYY-NNN).

Main Fields:



Student_ID, Semester, Course_Title, Course_Family, Course_Type, Section

Performance metrics (Attendance %, CT_Avg %, Midterm %, Final %, Lab_Final %)

Aggregate scores (Grand_Total %, Final_Grade, Completion_Flag, Performance_Comment)

Derived features for research: Cohort_Average %, Z_Score_Performance, Lab_Theory_Gap, Difficulty_Index


 

 

Key Findings from Data Analysis



Average Performance: 60 % overall; labs consistently 3–5 points higher than theory.

Completion Rate: ≈ 93 %, indicating high academic success.

Most Common Grades: B+ (125), A− (93), A (78).

Performance Distribution: Medium (41 %) dominates followed by Low (27 %) and High (20 %).

Curricular Progression: Later semesters show improvement in average marks and lower variance, reflecting effective curriculum refinement.


 

 

Research Applications

This dataset can be used for:



Longitudinal performance tracking in design and technology education

Comparative studies of theory vs practical learning outcomes

AI and ML model training for grade prediction and performance analytics

Curriculum evaluation and educational policy design

Instructor effectiveness and fairness assessment


Because the data originate from a single, consistent instructor, they offer a controlled environment for statistical and machine learning research in education.

 

 

Ethical Note

All student information has been anonymized.No personal data or identifiers are included.The dataset complies with DIU’s ethical standards for research and data sharing.

 

 

Keywords

creative-technology · educational data mining · longitudinal study · curriculum evolution · student performance · learning analytics · multimedia education · theory-lab correlation · academic assessment · creative pedagogy · art and technology · educational data science · Bangladesh · DIU · performance forecasting · data-driven education · teaching consistency · AI in education · design education · academic analytics