# University students dataset related to achievement, classroom practices, perceptions and attitudes of multimedia-based learning quantum physics

**Authors:** Pascasie Nyirahabimana, Evariste Minani, Mathias Nduwingoma, Imelda Kimeza, Nicolas Labrosse, Peter C. Samuels

PMC · DOI: 10.12688/f1000research.128013.1 · 2023-01-03

## TL;DR

This paper presents a dataset from Rwanda's University of Education on how quantum physics is taught and learned using traditional lectures and multimedia methods.

## Contribution

The dataset provides insights into teaching practices, student performance, and perceptions in quantum physics education.

## Key findings

- Data was collected using QPCS and COPUS to assess teaching methods and student understanding.
- QPAT was used to evaluate perceptions and attitudes before and after learning quantum physics.
- The dataset supports analysis of educational outcomes and classroom practices in STEM.

## Abstract

This dataset presents data collected to assess teaching and learning of quantum physics at the University of Rwanda - College of Education (UR-CE), Rwanda. Data were collected between August and November 2019 as the baseline, and between January and April 2022 under a quasi-experimental design. Three sets of data were collected. The first set was about students’ performance and conceptual understanding collected before and after teaching intervention (lecture method or multimedia-aided approach) using mainly Quantum Physics Conceptual Survey (QPCS). The second set documented classroom practices during teaching and learning using the Classroom Observation Protocol for Undergraduate STEM (COPUS). The last set is comprised of the data related to lecturers’ and students’ perceptions before teaching and learning quantum physics and students’ attitudes after learning Quantum physics. The Quantum Physics Attitude Test (QPAT) was mainly used to collect these data. The dataset is important to education stakeholders because university managers can visualize the status of teaching and learning outcomes, lecturers can reflect on the study, and researchers can use the data to analyze various independent variables.

## Full-text entities

- **Diseases:** Nicolas (MESH:C538105)
- **Species:** Homo sapiens (human, species) [taxon 9606]
- **Mutations:** Q19, Q22, Q20, Q18, Q12, Q23, Q21

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Source: https://tomesphere.com/paper/PMC11009567